Furthermore, our results encourage to conceive further studies on the role of sleep continuity, stability, and organization for sleep-dependent memory consolidation.

# **REFERENCES**

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brain, learning brain. The role of sleep for memory systems. *Neuroreport* 12, A111–A124.

a multifactorial geriatric syndrome. *J. Am. Geriatr. Soc.* 55, 1853–1866.

Vitiello, M. V. (2006). Sleep in normal aging. *Sleep Med. Clin.* 1, 171–176.

Vitiello,M. V., Foley, D., and Stratton, K. L. (2004). Prevalence of sleep complaints and insomnia in theVitamins And Lifestyle (VITAL) Study cohort of 77,000 older men and women. *Sleep* 27, 120.

Wauquier, A., and van Sweden, B. (1992). Aging of core and optional sleep. *Biol. Psychiatry* 31, 866–880.

Webb,W. B., and Dreblow, L. M. (1982). A modified method for scoring slow wave sleep in older subjects. *Sleep* 5, 195–199.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 03 March 2012; accepted: 16 June 2012; published online: 11 July 2012.*

*Citation: Conte F, Carobbi G, Errico BM and Ficca G (2012) The effects of pre-sleep learning on sleep continuity, stability, and organization in elderly individuals. Front. Neur. 3:109. doi: 10.3389/fneur.2012.00109*

*This article was submitted to Frontiers in Sleep and Chronobiology, a specialty of Frontiers in Neurology.*

*Copyright © 2012 Conte, Carobbi, Errico and Ficca. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

REVIEW ARTICLE published: 14 December 2012 doi: 10.3389/fneur.2012.00176

# NREM sleep oscillations and brain plasticity in aging

#### **Stuart Fogel 1,2, Nicolas Martin1,2,3, Marjolaine Lafortune1,3, Marc Barakat 1,2,3, Karen Debas 1,2 , Samuel Laventure1,2, Véronique Latreille1,3, Jean-François Gagnon1,3, Julien Doyon1,2 and Julie Carrier 1,2,3\***

1 Department of Psychology, Université de Montréal, Montréal, QC, Canada

2 Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, QC, Canada

3 Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montréal, QC, Canada

#### **Edited by:**

Géraldine Rauchs, Cyceron, France

#### **Reviewed by:**

Philippe Peigneux, Université Libre de Bruxelles, Belgium Erin J. Wamsley, Harvard Medical School, USA

#### **\*Correspondence:**

Julie Carrier, Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, 5400 Gouin Boulevard West, Montreal, QC, Canada H4J 1C5.

e-mail<EMAIL_ADDRESS>
#### **SLOW-WAVE NREM SLEEP OSCILLATIONS**

Non-rapid eye movement (NREM) sleep is characterized by different degrees of cortical synchronization, from lower in lighter sleep stages (1 and 2) to higher in deeper slow-wave sleep (SWS) stages. In mammals, more time spent awake results in higher cortical synchronization in NREM sleep, whereas synchronization dissipates as sleep progresses (Achermann et al., 1993). Cortical synchronization during NREM sleep is characterized by slow waves (SW). At the cellular level, SW have two phases: hyperpolarization [surface electroencephalogram (EEG) negative], during which cortical neurons are mostly silent (OFF period); and depolarization (surface EEG positive), during which most cortical neurons fire intensively (ON period; Steriade, 2006; Csercsa et al., 2010). Animal studies have demonstrated that, under high homeostatic sleep pressure, short periods of intense cortical activity (ON periods) alternate frequently with relatively long periods of neuronal silence (OFF periods). Conversely, under low homeostatic sleep pressure, long ON periods are interrupted by sporadic, short OFF periods (Vyazovskiy et al., 2009). Greater homeostatic sleep pressure is associated with higher SW density and amplitude, faster SW frequency (Hz), and steeper slope between negative and positive phases (Esser et al., 2007; Riedner et al., 2007; Bersagliere and Achermann, 2010; Carrier et al., 2011; Mongrain et al., 2011).

Electroencephalogram SW originate more frequently in prefrontal–orbitofrontal regions and travel in an anteroposterior direction (Massimini et al., 2004). Source modeling has shown that SW are more likely to originate in the insula and the cingulate gyrus, but often involve frontal areas and the precuneus (Murphy et al., 2009). Recent fMRI studies have confirmed that SW are associated with activation of the brainstem, inferior frontal gyrus, posterior cingulate cortex, and precuneus, which are important for sleep maintenance and cerebral plasticity (Dang-Vu et al., 2008).

# The human electroencephalogram (EEG) during non-rapid eye movement sleep (NREM) is characterized mainly by high-amplitude (>75µV), slow-frequency (<4 Hz) waves (slow waves), and sleep spindles (∼11–15 Hz; >0.25 s). These NREM oscillations play a crucial role in brain plasticity, and importantly, NREM sleep oscillations change considerably with aging.This review discusses the association between NREM sleep oscillations and cerebral plasticity as well as the functional impact of age-related changes on NREM sleep oscillations. We propose that age-related reduction in sleep-dependent memory consolidation may be due in part to changes in NREM sleep oscillations.

**Keywords: aging, sleep, NREM, declarative memory, procedural memory, cognition, slow waves, sleep spindles**

#### **SLOW WAVES AND CEREBRAL PLASTICITY**

Increasing evidence suggests that specific brain areas that are recruited for a particular task may require more slow-wave activity (SWA) than other brain areas (Kattler et al., 1994; Huber et al., 2004). According to the synaptic homeostasis hypothesis, cerebral plastic processes during wakefulness produce a net increase in synaptic strength¸ reducing the capacity for further potentiation. SWA during NREM sleep is associated with the downscaling of synaptic strength, which then allows for further synaptic potentiation and learning to occur following sleep (Tononi and Cirelli, 2006). Accordingly, waking behaviors associated with synaptic potentiation would be expected to increase SW, whereas waking behaviors associated with synaptic depression would be expected to reduce SW (Hanlon et al., 2009). In support of this hypothesis, somatosensory stimulation (Kattler et al., 1994) and training on a motor adaptation task (Huber et al., 2004) increased SWA the following night. Additional support for the synaptic homeostasis hypothesis in humans is supported by studies that applied TMS to manipulate neural excitability immediately prior to sleep (Huber et al.,2007,2008;Massimini et al.,2009). Results showed that TMSevoked increases in cortical responsiveness (potentiation) prior to sleep enhanced local SWA at central derivations, whereas TMSevoked decreases in cortical responsiveness (depression) reduced SWA locally during subsequent sleep. These findings provide compelling evidence in support of the synaptic homeostasis hypothesis (Huber et al., 2007, 2008) and the potential role of SW in brain plasticity (Tononi and Cirelli, 2006).

#### **CHARACTERISTICS OF SLEEP SPINDLE OSCILLATIONS**

Sleep spindles appear on the EEG as transient waxing and waning oscillations recurring approximately once every 3–10 s (Steriade, 2005). Two types of spindles have been described, distinguished by their frequency, scalp topography, and associated neural correlates identified using fMRI (Schabus et al., 2007). Slow spindles (∼11–13 Hz) predominate frontal derivations, whereas fast spindles (∼13–15 Hz) are more prominent at centro-parietal derivations (De Gennaro and Ferrara, 2003). Spindles reflect oscillatory activity in thalamocortical (TC) networks during NREM sleep, and may be associated with loss of perceptual awareness and consolidation of memory traces (Steriade, 2006). On the one hand, spindle oscillations have the intrinsic property to hyperpolarize TC cells through recurrent inhibition of reticular thalamic activity. This inhibitory spindle activity prevents afferent signals from being transmitted to the cortex, thus reducing cortical responsiveness to external stimulation during sleep (Steriade, 1994; Dang-Vu et al., 2011). On the other hand, postinhibitory rebound firing of TC cells causes the depolarization of cortical pyramidal neurons through rhythmic excitatory spike bursts. Cortical involvement is crucial in mediating spindle duration, as recently observed by intracellular recordings and computational models (Bonjean et al., 2011). Corticothalamic (CT) feedback can not only trigger spike bursts in reticular thalamic neurons to initiate a spindle oscillation, but it also can terminate the spindle by desynchronizing the thalamic network through depolarization of TC cells. Thus, spindle duration can be maintained by a synchronized spiking activity of the TC system, but is interrupted when thalamic and cortical firing fall out of phase.

# **SLEEP SPINDLES AND SYNAPTIC PLASTICITY**

The highly recurrent and coherent firing pattern associated with sleep spindles may promote plastic changes underlying learning and memory consolidation (Steriade and Timofeev, 2003). Indeed, spindle activity creates the ideal conditions for massive Ca2+ entry into depolarized dendrites of cortical neurons, a molecular event involved in synaptic plasticity and long-term potentiation (LTP; Sejnowski and Destexhe, 2000). In fact, LTP was successfully induced by trains of spindle activity in the rat somatosensory cortex (Rosanova and Ulrich, 2005), and the induction of LTP by tetanization of the rat corpus callosum increased the reliability of evoked spindle oscillations (Werk et al., 2005). Human neuroimaging studies have revealed that both fast and slow spindles are associated with increased hemodynamic responses in the thalamic nuclei and paralimbic areas (Schabus et al., 2007). Slow spindles are associated with greater cerebral activation in the superior frontal gyrus, whereas fast spindles recruit several cortical regions usually involved in sensorimotor processing as well as the hippocampus and mediofrontal areas. In addition, evidence from rodents has shown that prefrontal cortical spindles are temporally coordinated with high-frequency ripples in the hippocampus (Sirota et al., 2003). Since sleep spindles have been implicated in memory consolidation for hippocampal-dependent memory (see Fogel and Smith, 2011, for review), it is tempting to speculate that the temporal relationship between spindles and ripples may be a reflection the hippocampal-neocortical dialog thought to be required for memory consolidation (Siapas and Wilson, 1998). Taken together, these findings suggest that sleep spindles are associated with neurophysiological processes underlying brain plasticity.

# **AGE-RELATED CHANGES IN NREM SLEEP OSCILLATIONS**

Non-rapid eye movement sleep synchronization changes considerably with age, with a substantial reduction in SWS, an increase in lighter NREM sleep stages, and significant decreases in SWA (spectral power between 0.5 and 4 Hz) and sigma (13– 14 Hz) activity during NREM sleep, beginning as early as middle age (Carrier et al., 2001; Landolt and Borbély, 2001; Darchia et al., 2007). Previous studies have shown the greatest agerelated decreases in the spectral power of delta frequencies in frontal derivations (Munch et al., 2004; Robillard et al., 2010). Recently, we evaluated cortical mechanisms of NREM synchronization by characterizing changes in automatically detected SW in a large sample of young and middle-aged subjects (Carrier et al., 2011). Compared to young adults, older individuals had both lower SW amplitude and lower SW density, especially in prefrontal/frontal brain areas, where they most frequently originate (Massimini et al., 2004). In addition, older subjects had lower SW slope and longer SW positive and negative phases compared to young subjects. These age-related differences suggest that cortical neurons take more time to synchronously enter SW hyperpolarization and depolarization phases. This age-related reduction in slow activity during NREM sleep may have a negative impact on brain plasticity and sleep-dependent memory consolidation.

In addition to reduced SW during NREM sleep, spindle density, amplitude, and duration are reduced in older compared to young subjects (Guazzelli et al., 1986; Wei et al., 1999; Crowley et al., 2002), and this decline has been shown to be progressive with age (Principe and Smith, 1982; Nicolas et al., 2001). Yet spindle frequency shows a slight increase with age (Principe and Smith, 1982; Wei et al., 1999; Nicolas et al., 2001; Crowley et al., 2002). Spindle dynamics over the course of the night also change with age. While young subjects typically show increased sigma activity and spindle density from the beginning to the end of the night (Jankel and Niedermeyer, 1985; Aeschbach and Borbély, 1993), older subjects do not show this trend, or show it to a lesser extent (Guazzelli et al., 1986; Landolt et al., 1996; Wei et al., 1999).

Most studies exploring the effects of age on sleep spindles have restricted their analysis to a single EEG scalp derivation. Yet spindles are known to be topographically heterogeneous (Zeitlhofer et al., 1997). In fact, Nir et al. (2011) recently demonstrated that the majority of spindles occur independently across brain regions: most spindles appeared simultaneously in only a minority of brain areas, suggesting local rather than global modulation. Consistent with Nir et al. (2011), we reported that age effects differed across scalp location (Martin et al., 2013). Age-related decreases in spindle density and amplitude were most prominent at anterior derivations, whereas shorter duration was maximal at posterior derivations. Surprisingly, there were no age-related differences in spindle frequency at any derivations. It remains to be tested whether an age-related reduction in sleep spindle activity may have a negative impact on sleep stability (Dang-Vu et al., 2010), and whether this may underlie reduced age-related sleep-dependent memory consolidation.

# **ROLE OF NREM SLEEP OSCILLATIONS IN DECLARATIVE LEARNING**

The contributions of sleep to learning, memory, and brain plasticity have been studied for over 30 years (Smith et al., 1974; for a review, see Smith, 1985, 1996; Maquet, 2001; Walker, 2005; Stickgold and Walker, 2007; Diekelmann and Born, 2010). However, it remains a contentious topic (Stickgold and Walker, 2005). Indeed, it is unclear which sleep stages support the enhancement and/or consolidation of different memory systems.

Numerous recent studies propose the role of NREM sleep oscillations in declarative learning. It has been hypothesized that SW-dependent downscaling benefits memory by eliminating weak synapses (i.e., noise), thereby improving the signal-tonoise ratio in the synapses that were strongly potentiated during prior wakefulness (Tononi and Cirelli, 2006). According to another dominant hypothesis, declarative memory consolidation during sleep would occur during SWS via repeated reactivations (replay; see Bergmann et al., 2012; Diekelmann and Born, 2010, for recent reviews). This replay would benefit memory by transferring new memories from the hippocampus to the neocortex via hippocampal–neocortical dialog. In addition, Buzsáki (1989, 1998) has provided an alternative, but complementary viewpoint largely based on work done in rats. He suggests that during SWS, potentiated neurons of the hippocampus communicate with the neocortex via sharp-wave bursts. This communication is thought to underlie the hippocampal-neocortical transfer of information during sleep, leading to memory consolidation.

Studies suggest the key role of hippocampal/neocortical reactivation during early NREM in the consolidation of hippocampaldependent memories, such as declarative memory (Peigneux et al., 2004; Rasch et al., 2007). Compared to an equivalent period of wake, post-learning sleep has been shown to enhance declarative memory task retention (e.g., word pair retention; Tucker et al., 2006; Gais et al., 2007) and increase hippocampal activation during recall (Gais et al., 2007). In rats, the pattern of hippocampal place cell firing observed during spatial learning is reproduced during subsequent periods of SWS (Wilson and McNaughton, 1994), suggesting that memory reactivation may occur during SWS. In humans, Peigneux et al. (2004) observed that brain regions (including the hippocampus) that are active during virtual maze navigation are also active during SWS, suggesting that SWS may play a similar role for spatial memory reactivation in humans. Furthermore, declarative memory benefits mainly from sleep periods rich in SWS rather than rapid eye movement (REM) sleep (Plihal and Born, 1999; Gais and Born, 2004; Drosopoulos et al., 2005). A recent study (Rasch et al., 2007) has shown that spatial declarative memory can be enhanced when a contextual odor cue during learning is presented again during SWS. No enhancement was observed in the vehicleonly control condition or when the odor was re-presented during REM sleep. They concluded that the cue served to reactivate the memory trace during SWS, inducing enhanced SWS-dependent memory consolidation. Moreover, several studies have reported positive associations between the characteristics of NREM sleep including SWA, SW, and spindle oscillations and greater declarative memory retention after sleep (Clemens et al., 2005; Tucker and Fishbein, 2008; Tamminen et al., 2010; Van Der Werf et al., 2011; Wilhelm et al., 2011). However, several of these studies lacked a control night to distinguish state-like associations (spindle enhancement after learning and memory performance) from trait-like associations (inter-individual differences in cognitive abilities related to individual differences in spindles). It is necessary to consider inter-individual differences in spindle activity when investigating their night-to-night changes in response to learning, since spindles are associated with rather stable cognitive traits such as intellectual ability (Schabus et al., 2006; Fogel et al., 2007a). Nonetheless, several studies have clearly demonstrated that declarative learning prior to sleep increases SW and spindle oscillations, which are associated with overnight memory retention (Gais et al., 2002; Schabus et al., 2004, 2008; Schmidt et al., 2006; Molle et al., 2009). Moreover, boosting SW with transcranial direct current stimulation (TDCS) during sleep enhances verbal declarative memory retention (e.g., learning word pair associations; Marshall et al., 2006), and reducing SWA and slow sigma power during NREM sleep with TDCS reduces verbal declarative memory retention (Marshall et al., 2011), suggesting a causal relationship between SW and declarative memory. Thus, taken together, these results suggest that NREM sleep plays an important, if not a crucial role for declarative memory consolidation. Moreover, specific aspects of NREM sleep such as SW and spindles are correlated with memory consolidation and increase in a learning-dependent manner following declarative learning. Thus, suggesting that these events may play an active role in memory consolidation processes.

## **THE IMPACT OF AGE-RELATED CHANGES IN SLEEP ON DECLARATIVE MEMORY**

Currently, there is no clear consensus on the impact of aging on sleep-dependent declarative memory consolidation. One study identified reduced sleep-related consolidation of verbal memory in older subjects, associated with an age-related decrease in early-night SWS (Backhaus et al., 2007). Other studies found no age-related differences in sleep-dependent consolidation of verbal declarative memory (Aly and Moscovitch, 2010; Wilson et al., 2012). Discrepancies between these results may be attributable to statistical power issues and confounding factors, such as circadian effects on initial encoding and retrieval, and importantly, their interaction with age. Future studies should also use objective sleep measurements.

To our knowledge, only three studies have assessed whether NREM sleep oscillations are associated with declarative overnight memory retention in older participants. Higher SWA was linked with better overnight declarative memory retention in both healthy elderly adults and patients with mild cognitive impairment (Westerberg et al., 2012), whereas higher sleep spindle density was associated with better overnight visuospatial memory retention in older women (Seeck-Hirschner et al., 2012). A study in Alzheimer patients found no association between spindles and overnight memory retention, but instead found a positive relationship between memory performance prior to sleep and spindle intensity in subsequent sleep (Rauchs et al., 2008). However, these studies did not control for baseline sleep to distinguish between state-like associations versus trait-like associations. Nevertheless, another study has assessed a clear state-like association between

SWA and memory performance in healthy elderly patients, by experimentally reducing SWA using acoustic neurofeedback. This procedure was found to decrease the encoding of novel declarative information in older subjects (Van Der Werf et al., 2011). Taken together, these results suggest that NREM sleep spindles and SWA are related to declarative memory encoding and retention in older adults. The impact of age-related changes in sleep, and specifically the changes in NREM sleep oscillations on sleep-dependent declarative memory consolidation remains to be clarified.

 Among the most consistent findings in support of the role of sleep in memory consolidation is a series of studies demonstrating a link between procedural memory and stage 2 (Smith and MacNeill, 1994; Fischer et al., 2002; Walker et al., 2003; Fogel and Smith, 2006; Fogel et al., 2007b) and SWS sleep (Huber et al., 2004; Fogel et al., 2007b). Even a short amount of stage 2 sleep from daytime nap was found to be correlated with the delayed gains in performance on a procedural task (Korman et al., 2007). Given that procedural learning is a broadly defined and heterogeneous category, it is important to first distinguish between the types of task demands, in order to understand the link between procedural learning and sleep states. On the one hand, there is evidence that task performance depends more on REM sleep when the task involves the acquisition of entirely new skills or rules, or when individuals are not very proficient during acquisition (Smith and Weeden, 1990; Buchegger et al., 1991; Smith, 1993; Plihal and Born, 1997; Smith and Smith, 2003). On the other hand, procedural learning appears to depend more on stage 2 sleep when the task is cognitively simple and involves the refinement of skills that individuals may have already acquired to some extent (Fischer et al., 2002; Walker et al., 2002; Fogel and Smith, 2006; Fogel et al., 2007b). A number of studies support this hypothesis, originally proposed by Smith et al. (2004). The first direct evidence for this dissociation (Peters et al., 2007) revealed that when subjects were trained on a rotary pursuit procedural task and then subdivided into "high-skill" and "low-skill" groups, post-training spindle density was correlated with performance in the high-skill but not the low-skill group. Conversely, post-training changes in REM density were correlated with performance in the low-skill but not the high-skill group. Although correlational, this dissociation suggests that the type of sleep involved in memory consolidation depends on not only task type or task demands, but perhaps the individual's proficiency on the task.

Further support for this hypothesis comes from deprivation studies showing that stage 2 sleep disruption, but not REM deprivation in the last half of a night's sleep, leads to impaired performance on the pursuit rotor, a simple procedural task (Smith and MacNeill, 1994). In addition, studies investigating the impact of procedural learning on subsequent sleep found increased stage 2 sleep duration following simple procedural learning (Fogel and Smith, 2006; Fogel et al., 2007b). Moreover, sleep spindles in stage 2 sleep increased following simple procedural learning involving gross motor skills (Fogel and Smith, 2006; Fogel et al., 2007b) and motor sequence learning (MSL; Walker et al., 2002). Not only does sleep spindle density increase after performing a simple procedural task, but the morphology of the spindle also changes: sleep spindle amplitude and duration were shown to increase, and the increase in spindle density persisted into SWS (Fogel et al., 2007b). Taken together, these results suggest that the brain responds to simple procedural learning by increasing sleep spindles in a variety of ways, collectively leading to massively increased spindle activity over the course of a night's sleep. These changes are thought to reflect memory consolidation processes during sleep.

Initially, MSL relies on a variety of brain structures, including the cerebellar-cortical and striatal-cortical networks (for a review, see Doyon et al., 2003), and in some cases the hippocampus was found to be involved (Albouy et al., 2008). Once consolidated, procedural memory generally relies on a network of striatal-cortical structures (Doyon et al., 2003, 2009; Doyon and Benali, 2005). It was proposed that sleep spindles play a role in the transfer (or transformation) of information from brain structures involved in learning and in early consolidation of MSL (Walker et al., 2005). A recent study (Barakat et al., 2012) showed that spindle amplitude at frontal regions was correlated with offline gains in performance and changes in activity in the putamen over a sleep interval. These results provide some of the first direct evidence of the neural substrates involved in sleepdependent MSL consolidation that is specifically related to spindle activity.

The role of sleep in motor adaptation tasks is less clear. While we showed that procedural consolidation was not sleep-dependent (Doyon et al., 2009), Huber et al. (2004) observed increased SWA during sleep after subjects were trained on a more complex motor adaptation task. The increased SWA was circumscribed to a region of the posterior parietal cortex known to be activated by this task (Ghilardi et al., 2000). Motor adaptation task complexity may also explain discrepancies between studies.

# **THE IMPACT OF AGE-RELATED CHANGES IN SLEEP ON PROCEDURAL MEMORY**

Age-related changes in sleep may have a negative impact on sleepdependent procedural memory consolidation. In support of this hypothesis, Spencer et al. (2007) investigated the effect of a retention period filled with either sleep (or wake) on the performance of implicit and explicit versions of a MSL task in young and older subjects. They found that sleep-dependent gains in performance were observed in young but not in older subjects for both the implicit and explicit task versions of the task. In addition, Brown et al. (2009) has shown that sleep does not provide the same benefit to procedural memory consolidation in older adults as compared to young subjects. More specifically, they used an implicitly learned, cued version of the serial reaction time task to assess MSL for both sequence and random-order versions of

the task. Surprisingly, increased learning was observed in older subjects during the training session as compared to young subjects. However, only young subjects showed gains in performance over an intervening retention sleep period. It is important to note that this result was not attributable to age-related differences in speed or baseline performance. While these two studies suggest that sleep provides a benefit to procedural memory consolidation in young but not in older individuals, the characteristics of sleep were not investigated. A related study by Peters et al. (2008) suggested that age-related changes in sleep spindles were correlated with sleep-dependent procedural memory consolidation in young but not older adults. They found that learning a pursuit rotor task increased sleep spindles in young subjects but not in older subjects. Furthermore, whereas both young and older subjects improved with practice on the pursuit rotor task, the magnitude of the improvement was greater for young subjects when tested 1 week after initial learning, suggesting that young subjects performance was consolidated to a greater extent than older individuals. Better performance in young subjects at acquisition was associated with larger increases in spindle density, but not in older individuals. These results suggest that the age-related reduction in sleep spindles may underlie age-related deficits in procedural memory consolidation, however this hypothesis remains to be directly tested.

#### **REFERENCES**

#### **CONCLUSION**

One of the most marked changes in sleep in elderly populations is a reduction of SW and sleep spindles. NREM sleep oscillations are related to the reactivation of recently learned material, cerebral plasticity, and synaptic homeostasis. We propose that an age-related reduction in sleep-dependent memory consolidation may be due in part to changes in NREM sleep oscillations. Further research is required where NREM sleep oscillations are causally manipulated, to better disentangle the role that sleep spindles and SW have for memory consolidation in older subjects. Spindles and SW are generated by the neural oscillation between hyperpolarized and depolarized phases, believed to play a crucial role in brain plasticity (Steriade, 2006). At present, there is ample evidence to suggest that spindles and SW are involved in the consolidation of both declarative and procedural learning. Thus, it is difficult to conclude whether they serve dissociable roles for different memory systems, or not. Future research should evaluate whether age-related changes in spindles and SW interfere differently with declarative and procedural learning.

#### **ACKNOWLEDGMENTS**

Our review is supported by fellowships and salary supports from the Canadian Institutes of Health Research (CIHR) and the Fonds de recherche en Santé du Québec (FRSQ).

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 15 August 2012; accepted: 29 November 2012; published online: 14 December 2012.*

*Citation: Fogel S, Martin N, Lafortune M, Barakat M, Debas K, Laventure S, Latreille V, Gagnon J-F, Doyon J and Carrier J (2012) NREM sleep oscillations and brain plasticity in aging. Front. Neur. 3:176. doi: 10.3389/fneur.2012.00176*

*This article was submitted to Frontiers in Sleep and Chronobiology, a specialty of Frontiers in Neurology.*

*Copyright © 2012 Fogel, Martin, Lafortune, Barakat, Debas, Laventure, Latreille, Gagnon, Doyon and Carrier. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

# How aging affects sleep-dependent memory consolidation?

# *Caroline Harand1,2,3,4, Françoise Bertran1,2,3,5, Franck Doidy 1,2,3,4, Fabian Guénolé1,2,3,6, Béatrice Desgranges 1,2,3,4, Francis Eustache1,2,3,4 and Géraldine Rauchs 1,2,3,4\**

*1 INSERM, U1077, Caen, France*

*2 Université de Caen Basse-Normandie, UMR-S1077, Caen, France*

*3 Ecole Pratique des Hautes Etudes, UMR-S1077, Caen, France*

*4 CHU de Caen, U 1077, Caen, France*

*5 CHU de Caen, Unité d'évaluation et de traitement des troubles du sommeil, Caen, France*

*6 CHU de Caen, Service de Psychiatrie de l'Enfant et de l'Adolescent, Caen, France*

#### *Edited by:*

*Philippe Peigneux, University Libre de Bruxelles, Belgium*

#### *Reviewed by:*

*Axel Hutt, INRIA CR Nancy, France Timo Partonen, University of Helsinki, Finland*

#### *\*Correspondence:*

*Géraldine Rauchs, Unité de Recherche U1077, INSERM, Ecole Pratique des Hautes Etudes, Université de Caen Basse-Normandie, GIP Cyceron, Boulevard H. Becquerel BP 5229, 14074 Caen Cedex, France. e-mail<EMAIL_ADDRESS>
Memories are not stored as they were initially encoded but rather undergo a gradual reorganization process, termed memory consolidation. Numerous data indicate that sleep plays a major role in this process, notably due to the specific neurochemical environment and the electrophysiological activity observed during the night. Two putative, probably not exclusive, models ("hippocampo-neocortical dialogue" and "synaptic homeostasis hypothesis") have been proposed to explain the beneficial effect of sleep on memory processes. However, all data gathered until now emerged from studies conducted in young subjects. The investigation of the relationships between sleep and memory in older adults has sparked off little interest until recently.Though, aging is characterized by memory impairment, changes in sleep architecture, as well as brain and neurochemical alterations. All these elements suggest that sleep-dependent memory consolidation may be impaired or occurs differently in older adults. This review outlines the mechanisms governing sleep-dependent memory consolidation, and the crucial points of this complex process that may dysfunction and result in impaired memory consolidation in aging.

**Keywords: sleep, memory consolidation, aging, episodic memory, procedural memory, slow wave sleep, hippocampus**

In humans, sleep is characterized by the cyclic occurrence of two main physiological stages, namely non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. The former is usually subdivided in stage 1, stage 2, and slow wave sleep (SWS, stages 3 and 4) according to sleep depth. A sleep cycle lasts about 90–100 min, but SWS is most abundant during the first half of the night (up to 80% of sleep time), whereas REM sleep prevails in the second half of the night, alternating with stage 2.

Likewise, memory is not a unitary phenomenon but is composed of multiple systems. In this review, we will focus on episodic and procedural memory, the two memory systems that were most investigated in this field. Episodic memory, a subcomponent of declarative memory, supports the encoding, storage, and retrieval of life events set in a specific spatiotemporal context. It relies upon the hippocampus and surrounding cortices, as well as the frontal and parietal cortices (Dickerson and Eichenbaum, 2010 for review). In contrast, procedural memory enables the gradual acquisition of skills through training and is not necessarily accessed consciously (non-declarative memory). This memory system mainly relies on striato-cortical networks (Doyon et al., 2003).

Ample evidence indicates that sleep is a physiological state favoring memory consolidation, the process by which recently acquired and labile memory traces are progressively strengthened into more permanent and/or enhanced forms.We will first see how sleep-dependent memory consolidation occurs in young healthy subjects and then examine the changes that may compromise this process in older adults.

# **SLEEP-DEPENDENT MEMORY CONSOLIDATION IN YOUNG ADULTS**

Over the past two decades, numerous studies have provided evidence that sleep enhances memory consolidation. It has been initially shown that consolidation of declarative/episodic memories is enhanced by early SWS-rich periods of sleep whereas procedural memory rather benefits from late sleep during which REM sleep prevails (e.g., Plihal and Born, 1997). Other authors have pointed out the relevance of the succession of NREM–REM sleep cycles for memory consolidation regardless of the memory system the trace belongs to (Giuditta et al., 1995), potentially reflecting different processes operating on memory traces. Thus, Gais et al. (2000) showed that performance on a procedural visual discrimination task requires REM sleep but also a certain amount of preceding SWS. We also showed a complementary role of REM sleep and SWS in the consolidation of truly episodic memories. Whereas early SWS favors consolidation of the temporal aspects of episodic memories, REM sleep rather supports the consolidation of the spatial characteristics of these memories (i.e., location of words on a paper sheet) and the richness of contextual details (Rauchs et al., 2004). Consolidation of emotionally laden material also depends on REM sleep (Wagner et al., 2001).

More recently, studies have tried to unravel the electrophysiological substrates that mediate the beneficial effect of sleep on memory consolidation. Three main mechanisms may participate in this complex process of reorganization of memory traces during sleep: hippocampal reactivations, neocortical slow oscillations, and thalamo-cortical sleep spindles.

Using hippocampus-dependent spatial learning tasks in rodents, studies have reported that hippocampal place cells, activated during training, were reactivated in the same sequential order during subsequent periods of sleep (Wilson and McNaughton, 1994). Such neuronal reactivations were mainly observed during SWS (Skaggs and McNaughton, 1996; Kudrimoti et al., 1999) and more rarely during REM sleep (Louie and Wilson, 2001). These re-expressions are not limited to the hippocampus but also occur in the striatum and thalamus (Ribeiro et al., 2004) and in various neocortical areas (Qin et al., 1997;Ji and Wilson, 2007; Peyrache et al., 2009). This phenomenon was also observed in humans. Thus, Peigneux et al. (2004) showed that the hippocampus, activated during a spatial learning task, was reactivated during post-learning SWS. Interestingly, the amount of hippocampal activity during SWS was positively correlated with the overnight improvement of memory performance. More recently, Rasch et al. (2007) showed that reinstating during SWS an odor that was used as a contextual cue during learning, significantly enhanced episodic memory performance. This improvement was mediated by the hippocampus as odor re-exposure during SWS induced hippocampal activation that was even greater than during wakefulness.

Brain oscillations also participate actively in memory consolidation. Spindle density, spindle activity (index reflecting the activity or intensity of the spindle process), and the frequency of hippocampal ripples are increased during post-learning sleep (Fogel and Smith, 2011; Girardeau and Zugaro, 2011 for reviews). Learning experience increases the amplitude and slope of slow oscillations as well as slow wave activity during subsequent sleep (Diekelmann and Born, 2010 for review). These increases are associated to enhanced memory performance (e.g., Huber et al., 2004). In addition, inducing slow oscillation-like potential fields during post-learning sleep with transcranial direct current stimulation increases time spent in SWS, enhances EEG power in the slow oscillation frequency band and in the slow spindle band, and improves retention on an episodic memory task (Marshall et al., 2006).

# **THE HIPPOCAMPAL–NEOCORTICAL DIALOGUE: A MODEL OF SLEEP-DEPENDENT MEMORY CONSOLIDATION**

The different findings cited above are summarized in a model of sleep-dependent consolidation of declarative/episodic memories termed the *hippocampo-neocortical dialogue* (Buzsáki, 1996; **Figure 1A**). Briefly, during wakefulness, freshly learned information is temporarily encoded into neocortical and hippocampal networks. During SWS, memory traces are repeatedly reactivated within hippocampal networks. Reactivations are associated with sharp wave-ripple activity in the hippocampus. They are driven by slow oscillations which synchronize reactivations with the occurrence of thalamo-cortical spindles to drive the transfer of memory traces toward neocortical sites where they will be stored durably. Low levels of cortisol and acetylcholine are required to allow the replay and transfer of information (Diekelmann and Born, 2010). The existence of this process has been demonstrated in rodents (Bontempi et al., 1999) and in humans using fMRI (Takashima et al., 2006). It occurs more so in subjects who slept after learning than in subjects who were sleep-deprived (Gais et al., 2007). With time, the ventromedial prefrontal cortex plays an important role in the retrieval of consolidated memories.

For procedural memories, the mechanism could be very similar, involving however different brain areas such as the striatum and cortico-cerebellar networks (Doyon and Benali, 2005).

Besides this active system consolidation hypothesis, an alternative mechanism has been proposed as the "*synaptic homeostasis hypothesis*"(Tononi and Cirelli,2006)*.*During wakefulness, encoding of new information leads to an increase in synaptic strength in the brain. Such a state is not sustainable in terms of energy and tissue volume demands, and saturates the capacity of synapses for

new learning. Sleep, and more precisely slow oscillations, would serve to downscale synaptic strength to a baseline level, keeping nevertheless a difference between synapses that were potentiated during prior waking and those that were not. This hypothesis has found experimental support in animals (Vyazovskiy et al., 2008; Bushey et al., 2011) and in humans (Huber et al., 2004; Van Der Werf et al., 2009).

## **AGE-RELATED CHANGES IN SLEEP**

Sleep is substantially modified with increasing age. Older adults complain of early awakening in the morning and of difficulties to maintain continuous sleep. These difficulties are attested by an increase in the number of arousals during the night leading to sleep fragmentation. Total sleep time decreases, whereas time spent awake during the night or wake after sleep onset increase, thereby decreasing sleep efficiency (i.e., proportion of sleep time compared to time spent in bed). Sleep efficiency declines progressively with age, from approximately 86% at ages 37–54 to 79% over age 70 (Bliwise, 2005). In contrast, the frequency of daytime naps increases with age (Humm, 2001), potentially worsening the disruption of nocturnal sleep.

The most striking change in sleep architecture is the dramatic decrease in time spent in SWS across the adult lifespan. This decline is further accompanied by a reduction in the number and amplitude of slow oscillations (Petit et al., 2004). To a lesser extent, changes of phasic events in stage 2, such as K-complexes and sleep spindles are also observed (Crowley et al., 2002). Changes concerning REM sleep tend to become significant only after age 50. Thus, while the duration of REM sleep episodes increases through the night, it remains constant in older adults (Van Cauter et al., 2000), and the density of REMs is reduced (Darchia et al., 2003). Aging is further characterized by alterations of circadian rhythms (Bliwise, 2005) and reduced melatonin secretion (Pandi-Perumal et al., 2005). These two points may also have an impact on sleepdependent memory consolidation. Indeed, there is a circadian control of REM, but not of NREM sleep (Dijk and Czeisler, 1995; Dijk et al., 2000). Alterations of circadian rhythms may therefore result in changes in REM sleep. Melatonin is also intimately connected to memory consolidation as melatonin secretion is low during REM sleep (Birkeland, 1982) and high levels of this neurohormone inhibit memory formation during night (Rawashdeh et al., 2007).

#### **SLEEP-DEPENDENT CONSOLIDATION OF MEMORIES IN OLDER ADULTS**

Several elements suggest that sleep-dependent memory consolidation is altered with aging. First, older adults complain of memory impairment concerning mainly episodic memory. As mentioned above, consolidation of episodic memories relies upon SWS,which is also significantly reduced in older adults. Furthermore, with age some memory-related areas such as the frontal cortex undergo substantial structural and functional alterations (Kalpouzos et al., 2009). In contrast, the alteration of the hippocampus is more debated. This may be explained by the fact that the effects of age are not homogeneous within the hippocampus but rather concern its posterior part (Kalpouzos et al., 2009). More precisely,Mueller and Weiner (2009) reported a significant volume loss of CA3 (Mueller

and Weiner, 2009). Albeit not consistently found (e.g., La Joie et al. (2010) who reported a strong effect of age on the volume of the subiculum only), this point is of particular interest since during SWS, freshly encoded memories are reactivated in the CA3 subfield and then spread back into CA1 and cortical areas for effective consolidation (Hasselmo, 1999). Evidence of impaired hippocampal reactivations after spatial learning has been provided in aged rats compared to young animals (Gerrard et al., 2008). In addition, frontal white-matter tracts are also affected by age and may lead to impaired interactions between the prefrontal cortex and the hippocampus (Salat et al., 2005).

Age-related changes in some neuroendocrine and neuromodulatory systems may likewise have an impact on sleep quality and architecture, and therefore on memory consolidation. Thus, aging is accompanied by a cholinergic hypofunction (Schliebs and Arendt, 2006). Classically, acetylcholine levels are high at the waking state – allowing encoding of new information – and reach a minimum during SWS. The nadir of acetylcholine during early sleep allows the reactivation of memory traces within hippocampal networks and their transfer toward neocortical areas (Hasselmo, 1999; Gais and Born, 2004). Age-related cholinergic hypofunction may not have consequences on SWS-dependent consolidation of declarative memories but could have repercussions on consolidation of procedural memories during REM sleep, during which acetylcholine levels are high. Indeed, cholinergic activation during REM sleep appears to be a critical factor mediating the beneficial effect of sleep on procedural memories (Rasch et al., 2009). Finally, aging is also accompanied by changes in the hypothalamo-pituitary–adrenal axis schematically leading to increases in evening cortisol levels (Buckley and Schatzberg, 2005). As memory consolidation requires low cortisol levels during early sleep (Plihal and Born, 1999) and since the hippocampus contains a high density of cortisol receptors (Roozendaal et al., 2009), elevated glucocorticoid levels may impair hippocampal functioning and impede the hippocampo-neocortical transfer.

All the points mentioned above seem closely interlinked and underline the complexity to assess sleep-dependent memory consolidation in older adults (**Figure 1B**). A handful of studies have been conducted in recent years and have provided mixed results (see also Pace-Schott and Spencer, 2011 for review).

The first studies conducted in healthy older adults (without depressive disorder or dementia) have mainly focused on REM sleep, due to the potential pharmacological modulations by cholinergic agents. Thus, Schredl et al. (2001) revealed positive correlations between overnight improvements on a word-pair recall task and REM sleep augmentation following administration of the cholinesterase inhibitor donepezil before bedtime during six consecutive nights. More recently, Hornung et al. (2007) assessed the effect of various REM sleep manipulations (increase or decrease in time spent in REM sleep) on episodic and procedural memory consolidation. REM sleep suppression was achieved by selective deprivation of this sleep stage. REM sleep augmentation was performed either physiologically through REM sleep rebound after a selective REM sleep deprivation or pharmacologically by administration of donepezil. All these manipulations had no effect on episodic memory. In contrast, only pharmacological REM sleep augmentation had positive effects on procedural memory consolidation, suggesting that cholinergic activation, more than REM sleep *per se*, is a crucial component of REM sleep memory consolidation in elderly people.

Other studies tried to relate memory performance with sleep stages in healthy older adults, without administration of cholinergic medication. Thus, Mazzoni et al. (1999) reported correlations between recall performance on a word-pair learning task and the mean duration of NREM/REM sleep cycles and the proportion of time spent in cycles, indicating that sleep structure and the cyclic succession of NREM–REM sleep are important for sleepdependent memory consolidation in older adults. More recently, Backhaus et al. (2007) reported a decline of sleep-dependent consolidation of semantically related word-pairs, associated with a decrease of early nocturnal SWS in 50 years old subjects. Retention of word-pairs after sleep was also positively correlated with the amount of SWS. These results were not replicated with lists of unrelated word-pairs (Wilson et al., in press). Finally, Aly and Moscovitch (2010) showed that memory of stories and personal events in older adults benefited from a night of sleep as much as in young adults. In addition, the number of hours slept was positively correlated with recall performance. The personal relevance of the material is therefore a crucial factor favoring sleep-dependent memory consolidation.

Concerning procedural memory, Spencer et al. (2007) have proposed to young and older adults a serial reaction time task. Performance was assessed after a 12-h interval filled with sleep or wakefulness. Older and young adults showed similar degrees of initial learning. However, performance of older adults did not improve following sleep, indicating that sleep-dependent consolidation is impaired with age. These results were replicated and extended to middle-aged subjects who showed, compared to young adults, a reduced sleep-dependent change in performance (Wilson et al., in press).

In another study, Peters et al. (2008) compared young and older healthy participants on a simple pursuit rotor task. Both age groups exhibited significant improvements after a 1-week delay but the magnitude of this improvement was lower in the older group. Interestingly, spindle density significantly increased after learning in young but not in old subjects. Finally, Tucker et al. (2011) were the only ones to observe, using a motor sequence task (fingertapping), similar improvements after sleep in young and older adults, although full expression of these improvements was seen

#### **REFERENCES**

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later during the retest session in older participants. As reported by Peters et al. (2008), no correlation between overnight improvement and sleep architecture or spindle density was observed in the elderly group.

Overall, studies conducted in older adults have provided mixed results, especially for declarative memory. While one may logically expect an impaired consolidation of declarative memories during early sleep, due to the dramatic decrease in SWS, this result was not consistently found. Qualitative differences between tasks may influence sleep's effect on memory processing. Further studies are needed to better understand the effects of age. As for procedural memory, studies report that sleep-dependent consolidation is impaired, or that the magnitude of overnight changes in performance is reduced or that the behavioral expression of consolidation is delayed compared to young adults. The reduction of cholinergic activation during REM sleep during aging is one of the mechanisms likely to account for these quantitative or qualitative changes in memory performance.

# **CONCLUSION AND FUTURE DIRECTIONS**

Over the past 20 years, a plethora of studies have shown that sleep benefits memory consolidation in young healthy subjects. In older adults, severalfactors closely interlinked (decrease in SWS and slow oscillations, hypofunctioning and atrophy of frontal areas, reduction of the cholinergic tone, etc.) appear to hamper memory consolidation during sleep. Even if this field of research has sparked renewed interest during the last 5 years, some questions remain unanswered. In particular, studies combining sleep recordings, memory consolidation assessment, and functional neuroimaging acquisitions are needed to determine which brain areas will subserve the reorganization of memory traces during sleep and compensate for the frontal dysfunction. Studies in healthy older adults are also a prerequisite to understand how sleep-dependent memory consolidation is disrupted in neurological diseases such as Alzheimer's disease. Indeed, we observed in patients with mild Alzheimer's disease a specific decrease in the number and intensity of fast spindles (Rauchs et al., 2008) as well as a faster mean theta frequency during SWS (Hot et al., 2011). Both parameters were positively correlated with episodic memory performance confirming the deleterious impact of sleep changes on memory function and the existence of compensatory mechanisms in the early stages of the disease to maintain, albeit inefficiently, memory performance.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 07 November 2011; accepted: 10 January 2012; published online: 02 February 2012.*

*Citation: Harand C, Bertran F, Doidy F, Guénolé F, Desgranges B, Eustache F and*

*Rauchs G (2012) How aging affects sleepdependent memory consolidation? Front. Neur. 3:8. doi: 10.3389/fneur.2012.00008 This article was submitted to Frontiers in Sleep and Chronobiology, a specialty of Frontiers in Neurology.*

*Copyright © 2012 Harand, Bertran, Doidy, Guénolé, Desgranges, Eustache and Rauchs. This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits noncommercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.*

# Sleep apnea syndrome and cognition

# *Emilia Sforza\* and Frédéric Roche*

*Department of Clinical Physiology and Exercise, Pole NOL, CHU, Faculty of Medicine J, Lisfranc, UJM et PRES University of Lyon, Saint-Etienne, France*

#### *Edited by:*

*Julie Carrier, Université de Montréal, Canada*

#### *Reviewed by:*

*Hani M. Lababidi, King Fahad Medical City, Saudi Arabia Nadia Gosselin, Hopital du Sacre-Coeur de Montreal, Canada*

#### *\*Correspondence:*

*Emilia Sforza, Department of Clinical Physiology, EFCR, CHU Nord – Level 6, F – 42055 Saint-Etienne Cedex 2, France. e-mail<EMAIL_ADDRESS>
Obstructive sleep apnea (OSA) is a sleep-related breathing disorder characterized by repetitive episodes of airflow cessation resulting in brief arousals and intermittent hypoxemia. Several studies have documented significant daytime cognitive and behavioral dysfunction that seems to extend beyond that associated with simple sleepiness and that persists in some patients after therapeutic intervention. A still unanswered question is whether cognitive symptoms in OSA are primarily a consequence of sleep fragmentation and hypoxemia, or whether they coexist independently from OSA. Moreover, very little is known about OSA effects on cognitive performances in the elderly in whom an increased prevalence of OSA is present. In this review we will consider recent reports in the association between sleep apnea and cognition, with specific interest in elderly subjects, in whom sleep disturbances and age-related cognitive decline naturally occur. This will allow us to elucidate the behavioral and cognitive functions in OSA patients and to gain insight into age differences in the cognitive impairment. Clinically, these outcomes will aid clinicians in the evaluation of diurnal consequences of OSA and the need to propose early treatment.

**Keywords: obstructive sleep apnea, hypoxemia, sleep fragmentation, sleepiness, cognitive functions, elderly**

#### **INTRODUCTION**

Previous studies investigating the effects of obstructive sleep apnea (OSA) on alertness and cognitive functions have demonstrated that apnea recurrence, sleep fragmentation, and nocturnal hypoxemia may affect diurnal behavior, cognitive function, and wellbeing in these patients. A wide range of cognitive impairment has been identified in OSA patients, from attention and vigilance to memory and executive functions (Jackson et al., 2011). A still controversial point is which factor is mainly responsible for the cognitive impairment, given that up to now all considered factors, i.e., sleep fragmentation, sleepiness, apnea + hypopnea index (AHI), and hypoxemia, showed weak correlation with cognitive scores. Moreover, standard measures used in the clinical setting do not provide a reliable assessment of cognitive dysfunction, particularly in elderly subjects in whom it is difficult to differentiate between the age-related cognitive decline and the OSA-related impairment.

In this review, current knowledge about the type of cognitive dysfunction and the mechanisms underlying cognitive impairment are reviewed. Then we summarize what is know about cognitive dysfunction in the elderly in whom conflicting results are reported in the literature. These controversial data open discussion on the effect of therapy on cognitive dysfunction.

#### **INCIDENCE OF SLEEP APNEA SYNDROME**

Previous studies on sleep-related breathing disorders have focused on OSA to indicate a clinical entity characterized by repetitive episodes of complete or partial upper airway obstruction during sleep, inducing falls in oxygen saturation, and hypercapnia. To restore pharyngeal patency, patients have recurrent arousals from sleep, inducing sleep fragmentation and sleep discontinuity. The nocturnal hypoxemia and the recurrent arousals contribute to the development of sleepiness, hypertension, and cognitive impairment. Several factors increase the OSA risk such as genetic predisposition, obesity, upper airways shape (Pillar et al., 2000), craniofacial morphology (Schellenberg et al., 2000), hormonal influences (Bixler et al., 2001), and APOE4 phenotype. (Gottlieb et al., 2004)

Prevalence studies conducted over the past decades (Young et al., 1993; Quan et al., 1997; Bixler et al., 2001) have demonstrated that up to 5% of adults in Western countries are likely to have undiagnosed OSA. When we consider the severity of the disease stratified according to an AHI ≥ 5 and an AHI ≥ 15, the prevalence is from 3 to 28% for mildly severe cases (AHI > 5) and from 1 to 14% for moderately severe cases (AHI > 15). Results of cohorts in Wisconsin (1490 subjects; Young et al., 1993), Pennsylvania (932 subjects; Bixler et al., 1998, 2001), Spain (455 subjects; Duran et al., 2001), and from the Sleep Heart Health Study (6642 subjects; Quan et al., 1997) have demonstrated that 1 in every five adults has at least an AHI > 5 and 1 in every 15 has at least an AHI > 15. In community-dwelling older adults (age 65– 95 years), 81% of participants had an AHI > 5, with prevalence rates of 62% for an AHI > 10, 44% for an AHI > 20, and 24% for an AHI > 40 (Ancoli-Israel et al., 1991). Furthermore, in about 6000 older adults aged 63.5 years examined in the Sleep Heart Health study (Young et al., 2002) the authors found that in subjects aged 60–69 years, 32% had an AHI between 5 and 14, and 19% an AHI > 15. The rising trend in elderly subjects may be explained by changes in the anatomy or function of the upper airway (Eikermann et al., 2007) or by the coexistence of other medical disorders, i.e., diabetes, hypertension, and cardiovascular disease. However, recent data suggest that in the elderly, the cut-off point of an AHI of 5 or 10 (Ancoli-Israel and Coy, 1994) is a less sensitive marker in distinguishing subjects with or without OSA, an AHI ≤ 15 being present in the majority of healthy elderly subjects (Pavlova et al., 2008). This would support the hypothesis (Bliwise, 1994; Bixler et al., 1998; Pavlova et al., 2008; Lavie and Lavie, 2009) that the presence of a greater AHI in the older population may reflect either an age-related or an age-dependent disease, the first inducing the typical OSA symptoms, and the second having fewer clinical consequences.

## **COGNITIVE PERFORMANCE AND OSA**

Despite differences in the definition of OSA syndrome, several studies suggest that apnea recurrence, sleep fragmentation, daytime sleepiness, and nocturnal hypoxemia may induce an impaired cognitive function in OSA patients (Kim et al., 1997; Engleman and Joffe, 1999; Engleman et al., 2000; Adams et al., 2001) affecting attention, vigilance, memory, psychomotor performance, and executive function (Beebe et al., 2003; Aloia et al., 2004; Jackson et al., 2011). However, the presence and the extent of the cognitive changes in OSA subjects is still a matter of debate, the cognitive impairment worsening with disease severity but not linearly (Bedard et al., 1991; Boland et al., 2002). One study stresses that although sleepiness and hypoxemia might cause the neuropsychological deficits in OSA patients, the co-morbidities usually observed in these patients, i.e., cardiovascular diseases, obesity, physical inactivity, are more important than sleep apnea *per se* in affecting neurocognitive functions (Lim andVeasey, 2010). Finally, we have to consider that there is a large heterogeneity in the neuropsychological tests used in OSA patients making it difficult to compare the results (Décary et al., 2000).

Bearing in mind these limitations, in recent years special interest has focused on impairments of executive function that refer to the ability to develop and sustain an organized goal-directed and flexible approach to problem situations and to allow individuals to use adaptively their basic skills in a complex and changing external environment. This domain could be examined by tests demanding working memory, mental flexibility, planning, organization, and problem solving (Fulda and Schulz, 2001; Saunamaki and Jehkonen, 2007), tasks related to prefrontal cortex activity (Beebe and Gozal, 2002).  The controversial results may be partially explained by the severity of the disease, a minor cognitive impairment present in mild cases (Redline et al., 1997; Gosselin et al., 2006; Lis et al., 2008) and a greater deficit particularly in terms of executive function (Naëgele et al., 1995; Ferini-Strambi et al., 2003; Aloia et al., 2004) in severe cases (AHI circa 50).

In some studies supporting an executive dysfunction, performance deficits are not only reported in executive tests but in all examined cognitive abilities, (Naëgele et al., 1995; Feuerstein et al., 1997; Boland et al., 2002) suggesting a global cognitive impairment. As suggested by Verstraeten and Cludydts (2004), a methodological bias in the assessment of executive function is the lack of control of attention performances during the task, OSA patients showing a decline in the ability to sustain attention (Dinges and Kribbs, 1991) and having alertness instability (Doran et al., 2001). These two factors influence their performance. To overcome this limitation, two studies (Naëgelé et al., 2006; Lis et al., 2008) performed a combined evaluation of the executive function and alertness, measuring both n-back task and reaction time performances. Naëgelé et al. (2006) found that the untreated OSA group did not differ from the control group on dual tasks, e.g., combined auditory and visual digit span, that measure attentional resources. In contrast, they differ in the maintenance and processing components of working memory, suggesting that OSA subjects have difficulties in the processing of new information without attention deficit. In contrast, Lis and coworkers (Lis et al., 2008) performing a combined evaluation of executive function (i.e., repeated working memory), and alertness (i.e., repeated reaction time and subjective and objective sleepiness assessment), found a decline in reaction time on all tasks and a lowered accuracy only in the n-back test, suggesting a deficit in attention and more basic cognitive processes.

In terms of memory, studies in middle-aged patients (Salorio et al., 2002; Naëgelé et al., 2006; Yaouhi et al., 2009) found a minor impairment in word list learning and slightly decreased recall in a visual episodic memory task. Naëgelé et al. (2006) observed a free recall deficit in episodic memory but normal maintenance and recognition, confirming that the memory impairment in OSA is mild (Fulda and Schulz, 2001) and does not affect all memory processes (Naëgele et al., 1995; Salorio et al., 2002; Naëgelé et al., 2006).

#### **EFFECTS OF TREATMENT**

The current treatment of choice in OSA patients is the continuous positive airway pressure (CPAP) inducing a significant improvement in daytime sleepiness (Engleman et al., 1996), a reduction in occupational, work, and road accidents (Cassel et al., 1996) as well as an improvement in general health (Reimer and Flemons, 1999; Sanner et al., 2000; Barbé et al., 2001; McFadyen et al., 2001). When we consider the effect of CPAP on the cognitive function, we can see that while some deficits can be improved by therapy, other domains such as executive function remain unchanged (Matthews and Aloia, 2011). To exclude the effect of an insufficient treatment, Weaver et al. (2007) examined patients before and after CPAP therapy considering the objective adherence to treatment. They found that, even among patients using CPAP for more than 7 h per night, only 30% of these patients had a normalization of objective sleepiness and only 50% had normal results on the Functional Outcome of Sleep questionnaire. Zimmermann et al. (1996) studied verbal memory changes in 58 memory-impaired OSA patients after 3 months of CPAP therapy. They found a dose-response relationship between the level of CPAP adherence and the extent of improvement in verbal memory with, however, a third of the patients with 6 h adherence not showing any improvement. Similar data were reported byAntic et al. (2011) and Lau et al. (2010) confirming that even the most optimally treated OSA patients may not experience a complete reversal in attention and executive function, probably due to a permanent brain alteration in severe cases (Bedard et al., 1993; Naëgele et al., 1995; Feuerstein et al., 1997; Ferini-Strambi et al., 2003).

# **PATHOGENESIS OF COGNITIVE IMPAIRMENT**

In large epidemiological studies (Kim et al., 1997; Boland et al., 2002) analyzing memory and cognitive and psychomotor functions, some authors found that OSA severity, as indicated by the AHI, was significantly related to diminished psychomotor efficiency with, however, a weak correlation. Similar findings were described in the Danish MONICA study cohort comprising of 848 participants (Jennum and Sjol, 1994) in that an AHI > 5 was associated with self-assessed concentration problems but not with memory impairment. Today there is general agreement that not apnea recurrence in itself, but the associated sleep fragmentation and nocturnal hypoxemia, are the key factors affecting cognitive function in patients with OSA (Bedard et al., 1991; Redline et al., 1997; Engleman and Joffe, 1999; Daurat et al., 2008). This hypothesis has been confirmed by animal studies showing that both intermittent hypoxia (Kalaria et al., 2004; Xu et al., 2004) and sleep fragmentation (Tung et al., 2005; Nair et al., 2011), two essential features of the OSA syndrome, can independently lead to neuronal loss in the hippocampus and prefrontal cortex, areas closely associated with memory processes and executive functions.

According to Beebe and Gozal (2002), the prefrontal cortex is selectively susceptible to both sleep deprivation and hypoxemia. The prefrontal cortex may exert modulatory influences on lowerlevel processes sub-served by posterior cortical regions (Knight et al., 1999) that have biological vulnerability to sleep loss and sleep fragmentation (Horne,1993,1998;Thomas et al.,2000).Moreover, attention control is believed to rely on the prefrontal cortex and its interaction with the thalamus that we know is implicated in alertness, vigilance and selective and sustained attention (Portas et al., 1998;Jones and Harrison, 2001). If so, a combined deficit in executive and attention skills is to be expected in OSA patients. It might be suggested that at the beginning of the disease when patients are asymptomatic or showing minimal cognitive impairment, a chemical and dysfunctional brain injury could occur, reflected in increased compensatory frontal activation during executive tasks. During the progression of the disease, the chronic recurrence of apneas and the progressive worsening of the hypoxemic stimulus will induce structural brain lesions, explaining the greater cognitive alteration, the impairment of executive tasks, and the lack of cognitive normalization after treatment.

# **ROLE OF SLEEPINESS AND SLEEP FRAGMENTATION**

Experimental reports (Dinges and Kribbs,1991;Dinges et al.,1997; McCoy and Strecher, 2011) in healthy subjects assumed that cognition varies in parallel with changes in alertness, supporting the hypothesis that sleepiness is the major factor underlying performance impairment. However, when we consider OSA patients, there is no linear relationship between the degree of sleepiness and cognitive impairment, suggesting that in these patients cognitive impairment is related more to an instability state (Doran et al., 2001) acting on vigilance and attention than on executive dysfunction (Verstraeten and Cludydts, 2004). Moreover, OSA patients without notable cognitive impairment have been found in approximately half of a non-clinical population (Quan et al., 2006) as well as in an elderly community population (Bixler et al., 1998; Sforza et al., 2010), suggesting that there are some factors protecting OSA patients from substantial cognitive deficits (Beebe

et al., 2003). These factors include sleep deprivation tolerance (Mu et al., 2005) and cognitive reserve that allow sleep-deprived subjects (Drummond et al., 2005) or patients (Stern, 2002; Alchanatis et al., 2005) to maintain intact performances. The hypothesis of a reserve brain activation has been confirmed by functional imaging studies (fMRI) using cognitive tasks (Thomas et al., 2005; Ayalon et al., 2006a, 2009a,b; Castronovo et al., 2009; Yaouhi et al., 2009; Zhang et al., 2011) showing a decrease in frontal cortex metabolism in the resting condition and an increase in neural activation in frontal lobe, cingulate, thalamus, and hippocampus during task activation.

# **ROLE OF HYPOXEMIA**

From large population studies (Telakivi et al., 1988; Jennum and Sjol, 1994;Kim et al., 1997;Redline et al., 1997;Horne, 1998; Engleman and Joffe, 1999; Adams et al., 2001; Beebe and Gozal, 2002) it would seem that, while the excessive daytime sleepiness and sleep fragmentation influences attention, the hypoxemia contributes to frontal impairment, and executive dysfunction. Using an experimental model of OSA (Gozal et al., 2001) in rats exposed for 2 weeks to intermittent hypoxia, the authors found a neuronal loss or apoptosis indicating a greater vulnerability of the frontal lobe to hypoxemia. Moreover, Mc Coy et al. (2010) recently showed an impaired allocation of attentional resources in rats exposed one week to intermittent hypoxia. Since frontal lobes have dense connections to other cortical areas (Fulda and Schulz, 2001; Beebe and Gozal, 2002) the primary frontal dysfunction will progressively affect other brain regions, inducing more complex cognitive dysfunction. If so, the persistence of a neuropsychological deficit after CPAP (Bedard et al., 1993; Zimmermann et al., 1996; Lau et al., 2010; Antic et al., 2011; Matthews and Aloia, 2011) may be explained. However, if hypoxemia alone may explain the cognitive dysfunction in severe cases, in mildly affected patients (Redline et al., 1997; Lim and Veasey, 2010) the interacting effect of cognitive reserve (McCoy and Strecher, 2011), intelligence level (Quan et al., 2006), and depression (Sachs-Ericsson et al., 2005; Cross et al., 2008) needs to be considered.

# **NEUROIMAGING DATA**

Within the past decade, the growing prevalence of functional neuroimaging studies has induced an increased interest in the association between OSA and cognition and has opened a new window on the role of cerebral metabolic and circulatory impairment in neuropsychological dysfunction. Routine neuroimaging studies (MRI) in OSA patients have reported inconsistent findings (Davies et al., 2001) or silent vascular lesions (O'Donoghue et al., 2005; Nishibayashi et al., 2008) suggesting a general brain impairment. Voxel-based morphometry (Macey et al., 2002, 2008) have demonstrated a gray matter reduction in brain regions that regulate memory and executive functions, i.e., cingulum, frontal, parietal and temporal areas, and hippocampus.  Interestingly, in the Yaouhi et al. (2009) study, the authors found that gray matter density and metabolic levels were altered even in patients without cognitive disturbances, suggesting that cerebral changes precede the onset of neuropsychological deficits. Canessa et al. (2011) performed a neuropsychological testing and voxel-based morphometry in 17 severely affected patients before and after 3-months of CPAP treatment. Prior to treatment the patients had poorer performances in some cognitive tests and a reduction of gray matter volume in the hippocampus, parietal cortex and frontal areas. After treatment there was an improvement in memory, attention and executive functions that paralleled the gray matter increase in the hippocampus and frontal areas, without, however, a complete normalization both in cognitive function and gray matter density. In contrast, a recent study using magnetic resonance spectroscopy (O'Donoghue et al., 2012) shows that OSA patients had decreased metabolic activity in the frontal lobe and hippocampus that did not correlate with cognitive scores and did not change after 6 months of CPAP therapy.

To date, any significant relationship between cognitive scores and brain morphological alteration has been reported, except for a slight association between hippocampal gray matter decrease, and memory dysfunction (Yaouhi et al., 2009) and the gray matter increase in the left entorhinal cortex in patients showing a reduction of errors at the Stroop test (Canessa et al., 2011). The lack of significant relationship between morphological changes and neuropsychological scores may be explained by the interplay of compensatory mechanisms reflected by the increased activation of frontal areas and hippocampus in fMRI and PET studies (Castronovo et al., 2009; Yaouhi et al., 2009).

#### **COGNITIVE FUNCTION IN ELDERLY OSA SUBJECTS**

Although several studies have shown that the prevalence of OSA increases with age (Bixler et al., 1998, 2001; Young et al., 2002) there is debate as to whether OSA phenotype in older adults is equal to that in middle-aged patients (Ancoli-Israel et al., 1991; Phillips et al., 1992; Bliwise, 1994; Young, 1996) and whether the aging *per se* increases the susceptibility to cognitive dysfunction (Alchanatis et al., 2008; Mathieu et al., 2008). A first epidemiological survey (Boland et al., 2002) has not revealed any evidence of a dose-response relationship between the respiratory disturbance index and cognitive function score in a cohort of 837 men and

#### **REFERENCES**

(2008). Comparison of cognitive performance among different age groups in patients with obstructive sleep apnea. *Sleep Breath.* 12, 17–24.

923 women over 60 years old. Studies performed on sleep clinic patients have given conflicting results, some authors reporting a relationship between cognitive impairment and OSA (Kim et al., 1997; Cohen-Zion et al., 2001) in older patients, while others do not (Phillips et al., 1992). Severity of the disease seems to play a key role in cognitive deficit, older patients being more likely to have a cognitive deficit in attention and executive function when the AHI is greater than 30 (Aloia et al., 2004). In a prospective study conducted on 289 women aged 82 with mild cognitive impairment and dementia (Yaffe et al., 2011) the authors demonstrated that older OSA women had an increased risk of developing mild cognitive impairment or dementia after 5 years, stressing the key role of OSA in the increased cognitive risk. When treated, patients without (Aloia et al., 2003) or with mild dementia (Ancoli-Israel and Coy,1994;Ayalon et al.,2006b;Ancoli-Israel et al.,2008;Cooke et al., 2009) have a slower cognitive deterioration and sometimes even an improvement thus stressing the link between cognitive decline and OSA.

To explain the link between cognitive decline and OSA in the elderly, an emerging hypothesis is that the chronic intermittent hypoxemia observed in OSA patients might be seen as a factor which expedites the effects of other conditions, particularly aging, that are known to cause brain atrophy or damage in the prefrontal cortex and hippocampus. Ayalon et al. (2010) demonstrate that the presence of OSA accelerates the age-related decline in cognitive performances in middle-aged patients (>45 years) showing a decreased activation of the frontal gyrus, hippocampus, and parietal lobe during a sustained attention and a verbal encoding task. These results are supported by a morphological study (Torelli et al., 2011) showing a correlation between age and volumes of the total and left hippocampus, amygdale, and brain parenchyma in OSA patients. If the presence of OSA can be seen as a factor which accelerates the process of brain aging, an early treatment should be proposed (Pack and Maislin, 2001; Ancoli-Israel, 2007) to prevent or to slow the cognitive deterioration (Cohen-Zion et al., 2001).

In summary, the connection between OSA and cognition, the increase of OSA risk in general population and the increasing aging population underline the need of further studies on the risk of development of a cognitive impairment in OSA patients and on the long-term effect of therapy on the age-related cognitive decline.

sleep apnea. *J. Psychosom. Res.* 54, 71–76.

case-controlled data. *Sleep* 26, 298–307.

J. (2000). Cognitive function in the sleep apnoea-hypopnoeas syndrome. *Sleep* 23, S102–S108.

and cerebral changes after intermittent hypoxia in rats. *Respir. Physiol. Neurobiol.* 140, 53–62.

	- Mc Coy, J. G., McKenna, J. T., Connolly, N. P., Poea, D. L., Ling, L., McCarley, R. W., and Strecker, R. E. (2010). One week of exposure to intermittent hypoxia impairs attentional setshifting in rats. *Behav. Brain Res.* 210, 123–126.
	- McCoy, J. G., and Strecher, R. E. (2011). The cognitive cost of sleep lost. *Neurobiol. Learn. Mem.* 96, 564–582.

sleep-disordered breathing. *J. Appl. Physiol.* 98, 2226–2234.

S. (1993). The occurrence of sleep-disordered breathing among middle-aged adults. *N. Engl. J. Med.* 328, 1230–1235.

obstructive sleep apnea. *Chest* 130, 1772–1778.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 19 November 2011; paper pending published: 15 March 2012; accepted: 07 May 2012; published online: 29 May 2012.*

*Citation: Sforza E and Roche F (2012) Sleep apnea syndrome and cognition. Front. Neur. 3:87. doi: 10.3389/fneur.2012.00087*

*This article was submitted to Frontiers in Sleep and Chronobiology, a specialty of Frontiers in Neurology.*

*Copyright © 2012 Sforza and Roche. This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits non-commercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.*

MINI REVIEW ARTICLE published: 17 May 2012 doi: 10.3389/fneur.2012.00082

# *Jean-François Gagnon\*, Josie-Anne Bertrand and Daphné Génier Marchand*

*Center for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Montréal, QC, Canada*

#### *Edited by:*

*Julie Carrier, Université de Montréal, Canada*

#### *Reviewed by:*

*Monica M. C. Gonzalez, Instituto Ferrero de Neurologia y Sueno, Argentina Angel Nunez, Universidad Autonoma de Madrid, Spain*

#### *\*Correspondence:*

*Jean-François Gagnon, Centre d'Études Avancées en Médecine du Sommeil, Hôpital du Sacré-Coeur de Montréal, 5400 boul. Gouin ouest, Montréal, QC, Canada H4J 1C5. e-mail: gagnon.jean-françois.2@ uqam.ca*

Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by excessive muscle activity and undesirable motor events during REM sleep. RBD occurs in approximately 0.5% of the general population, with a higher prevalence in older men. RBD is a frequent feature of dementia with Lewy bodies (DLB), but is only rarely reported in Alzheimer's disease. RBD is also a risk factor for α-synuclein-related diseases, such as DLB, Parkinson's disease (PD), and multiple system atrophy. Therefore, RBD has major implications for the diagnosis and treatment of neurodegenerative disorders and for understanding specific neurodegeneration patterns. Several markers of neurodegeneration have been identified in RBD, including cognitive impairments such as deficits in attention, executive functions, learning capacities, and visuospatial abilities. Approximately 50% of RBD patients present mild cognitive impairment. Moreover, RBD is also associated with cognitive decline in PD.

**Keywords: sleep, cognition, elderly, REM sleep behavior disorder, mild cognitive impairment, Parkinson's disease, dementia with Lewy bodies**

#### **DESCRIPTION OF RBD**

Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by the presence of undesirable and often violent motor manifestations during REM sleep, such as laughing, shouting, reaching, gesturing, arm flailing, punching, kicking, sitting up, or leaping out of bed (Schenck et al., 1986). These behaviors are generally associated with dream contents: individuals appear to act out their dreams. The violent nature of some dream-enactment behaviors may cause severe injury to individuals with RBD or their bed partners (Schenck et al., 2009). RBD generally affects adults aged 50 years and over, with an estimated prevalence of about 0.5% in the general population (Ohayon et al., 1997). This rate may increase to about 7% in people aged 70 and over (Boot et al., 2012). RBD is predominantly reported in men, at a proportion of around 85% (Bodkin and Schenck, 2009). The reasons for this gender disproportion remain unclear (selection bias or a biological effect).

In normal individuals, muscular activity during REM sleep is suppressed (atonia). In RBD, the mechanisms responsible for atonia are altered, causing abnormally excessive chin or limb electromyographic activity during REM sleep (Montplaisir et al., 2010). This leads to the vigorous behavioral manifestations reported by patients and relatives, which can also be seen on infrared videotape in a sleep laboratory. The RBD diagnosis is confirmed by polysomnographic recording, including analyses of electromyographic activity during REM sleep (American Academy of Sleep Medicine, 2005). Polysomnographic recording has the additional advantage of eliminating other sleep disorders that are confused with RBD, such as somnambulism or moderate to severe sleep apneas. Questionnaires are also available for assessing clinical RBD (Li et al., 2010; Boeve et al., 2011). For treatment of RBD symptoms, clonazepam and melatonin are the two most commonly used pharmacological agents (Gagnon et al., 2006a; Aurora et al.,2010). Changing the sleep environment is also recommended to prevent injuries to the patient and the bed partner. Although the pathophysiology of RBD in humans is still debated, anomalies in the pontomedullary brainstem neural networks responsible for the suppression of muscle tone during REM sleep (i.e., the sublaterodorsal nucleus or coeruleus/subcoeruleus complex) are strongly suspected (Boeve et al., 2007; Luppi et al., 2011).

# **RBD SUBTYPES**

There are different forms of RBD. The acute form is triggered by certain psychotropic drugs (pharmacology-induced RBD), such as antidepressants (Gagnon et al., 2006a). RBD is also strongly associated with certain neurological disorders (symptomatic RBD), including narcolepsy, Machado–Joseph disease, amyotrophic lateral sclerosis, epilepsy, multiple sclerosis, and Guillain–Barre syndrome (Gagnon et al., 2006b; Iranzo et al., 2009). Symptomatic RBD can also be induced by focal lesions (vascular or inflammatory), tumors, or neurodegenerative processes in brainstem regions responsible for normal REM sleep muscle atonia (Gagnon et al., 2006b;Iranzo et al., 2009; Limousin et al., 2009). In fact, RBD is very frequent in synucleinopathies, a class of neurodegenerative diseases characterized by abnormal deposition of α-synuclein proteins. For instance, RBD affects about 33–46% of patients with Parkinson's disease (PD; Gagnon et al., 2002; Sixel-Döring et al., 2011), 75% of patients with dementia with Lewy bodies (DLB; Ferman et al., 2011), and almost 100% of patients with multiple system atrophy (MSA; Vetrugno et al., 2004). Synucleinopathies share a common brainstem neurodegeneration with RBD, which may explain their strong association. On the other hand, RBD is rare in tau-related diseases such as Alzheimer's disease (AD), progressive supranuclear palsy, corticobasal degeneration, and frontotemporal dementia (Gagnon et al., 2006b; Iranzo et al., 2009). RBD is therefore a useful feature to consider for differential diagnosis between DLB and AD. In fact, inclusion of RBD as a core clinical feature improves the DLB diagnosis accuracy (Ferman

et al., 2011). Finally, RBD can appear alone ("idiopathic" RBD or iRBD) without any associated condition (Gagnon et al., 2006b; Iranzo et al., 2009). However, the term "idiopathic" is subject to challenge because iRBD may be a risk factor for synucleinopathies (Fantini et al., 2005; Ferini-Strambi, 2011).

# **"IDIOPATHIC" RBD AS A RISK FACTOR FOR SYNUCLEINOPATHIES**

Three longitudinal studies performed in a sleep disorders center found that RBD is a significant risk factor for developing synucleinopathies. Schenck et al. (1996) reported in 29 iRBD patients that 38% had parkinsonism 3.7 years after RBD diagnosis. Another study in 44 iRBD patients found that 36% developed a synucleinopathy 5.1 years following RBD diagnosis (Iranzo et al., 2006). Of the converted patients, 56% developed PD, 38% DLB, and one patient developed MSA. Four additional patients with iRBD met the criteria for mild cognitive impairment (MCI) at follow-up. In 2009, our group published the follow-up results on a large cohort of 93 patients with iRBD: 28% developed a neurodegenerative disease 4.8 years following RBD diagnosis (Postuma et al., 2009a). Of the diseased patients, 54% developed PD, 42% DLB, and one patient developed MSA. Using a life table (survival) analysis, the risk for developing a synucleinopathy in iRBD patients was estimated at 18% after 5 years, 41% after 10 years, and 52% after 12 years (Postuma et al., 2009a).

In the only population-based study to date, Boot et al. (2012) followed 651 cognitively intact participants, including 44 individuals with baseline clinical iRBD. After a median of 3.8 years, only 2% of iRBD patients developed PD, whereas 32% met MCI criteria. Although the conversion rate was far lower than in previous clinical studies, the iRBD patients had a 2.2-fold increased risk of developing MCI or PD over non-RBD patients. A potential explanation for the link between RBD symptoms and the development of synucleinopathies comes from studies on the pathological progression of synucleinopathies (Braak et al., 2003; Halliday et al., 2011). In the first stages, before clinical motor symptoms appear, Lewy bodies and Lewy neuritis can be found in brainstem areas involved in RBD pathophysiology. The gradual progression of the neurodegeneration to more rostral brain structures would subsequently cause symptoms characteristic of synucleinopathies. This may explain why RBD is an early symptom in certain patients with synucleinopathies. These results show the importance of performing neurological and neuropsychological assessments to detect early signs of a synucleinopathy or MCI in iRBD patients, particularly for those referred to a sleep disorders center.

#### **MARKERS OF NEURODEGENERATION IN "IDIOPATHIC" RBD**

Several markers of synucleinopathies have been identified in iRBD. Recent studies have reported that the severity of the loss of REM sleep muscle atonia (Postuma et al., 2010), olfaction and color vision impairments (Postuma et al., 2011a), substantia nigra hyperechogenicity, and decreased striatal dopamine transporters uptake (Iranzo et al., 2010a) can predict the development of synucleinopathies in iRBD (Postuma et al., 2011b). Other studies have found cognitive (Ferini-Strambi et al., 2004; Gagnon et al., 2009), subtle motor (Postuma et al., 2009b), waking EEG (Fantini et al., 2003), autonomic (Miyamoto et al., 2006; Postuma et al., 2009b), and functional and structural neuroimaging (Unger et al., 2010; Hanyu et al., 2011; Scherfler et al., 2011; Vendette et al., 2011) anomalies in iRBD, similar to those reported in synucleinopathies (Gagnon et al., 2006b; Postuma et al., 2011b).

## **COGNITIVE DECLINE IN RBD**

#### **POOR COGNITIVE PERFORMANCE IN "IDIOPATHIC" RBD**

Increasing evidence shows that iRBD patients perform poorly on neuropsychological tests (Ferini-Strambi et al., 2004; Massicotte-Marquez et al., 2008; Terzaghi et al., 2008; Gagnon et al., 2009; Marques et al., 2010; Fantini et al., 2011). However, results vary across studies depending on which cognitive domain is impaired (**Table 1**). Population heterogeneity, small sample size, and the use of different cognitive tasks with variable sensitivity to detect deficits and variable specificity to a cognitive domain may explain these discrepancies. In general, attention, executive functions, episodic verbal memory (mainly free recall capacities), and nonverbal learning are the most affected domains in iRBD (Ferini-Strambi et al., 2004; Massicotte-Marquez et al., 2008; Terzaghi et al., 2008; Gagnon et al., 2009; Marques et al., 2010; Fantini et al., 2011). Additionally, some studies reported in iRBD anomalies in visuospatial/visuoperceptive abilities (Ferini-Strambi et al., 2004; Iranzo et al., 2010b; Marques et al., 2010; Fantini et al., 2011), but this remains controversial (Massicotte-Marquez et al., 2008; Terzaghi et al., 2008; Gagnon et al., 2009). In fact, the presence of visuospatial (or non-verbal learning) impairment appears to be related to the extent of cognitive decline in iRBD patients (Iranzo et al., 2006; Molano et al., 2010; Fantini et al., 2011), as reported in RBD-associated neurodegenerative diseases such as PD or DLB (Ferman et al., 2002; Gagnon et al., 2009). On the other hand, language and praxis appear to be well preserved in iRBD, although these functions have received little research attention.

**Table 1 | Controlled studies on cognitive performance in "idiopathic" rapid eye movement sleep behavior disorder.**

*a,bShare common participants; Yes* = *patients show poorer performance than controls (p* < *0.05); No* = *similar performance between patients and controls.*

#### **Box 1 Case report.**

Mister X is a 62-year-old man referred to a memory clinic for cognitive assessment. He complains about his reduced abilities to concentrate and recall information. The scan is normal. No vascular risk factors or major psychiatric symptoms are found. The neuropsychological exam shows mild deficits in cognitive tests assessing attention and episodic verbal memory (affected free recall capacities with preserved recognition). Daily life activities are reported as satisfactorily accomplished. Based on this clinical profile, Mister X meets the criteria for mild cognitive impairment.When questioned about his sleep, he reports the presence of violent behaviors associated with vivid dreams. His wife confirms this and reports that she sleeps in a separate bed to avoid potential injury. Mister X is referred to a sleep disorders center to confirm a diagnosis of rapid eye movement (REM) sleep behavior disorder (RBD). The polysomnographic recording shows excessive chin muscle tone during REM sleep with no other anomalies. When these results are combined with the reports of dream-enactment behaviors, Mister X meets the RBD diagnosis criteria. Unfortunately, subsequent assessments at the memory clinic reveal cognitive decline, particularly in executive functions and visuospatial abilities. In addition, at the last visit, the patient reports a significant impact of the cognitive impairment on his daily life activities and shows signs of parkinsonism. The diagnosis of probable dementia with Lewy bodies is confirmed.

#### **MCI IN "IDIOPATHIC" RBD**

Mild cognitive impairment is known to be an intermediate state between normal cognitive functioning and dementia (Gauthier et al., 2006). MCI can be diagnosed according to the following criteria: (1) subjective cognitive complaint by the patient or a relative, (2) cognitive decline on neuropsychological testing compared to age- and education-equivalent individuals, and (3) preserved daily life activities (Gagnon et al., 2009; Albert et al., 2011). MCI can be subdivided into different subtypes according to the number (single-domain vs. multiple-domain) and nature (amnestic vs. non-amnestic) of the cognitive domains impaired (Petersen and Morris, 2005). MCI is a risk factor for dementia such as AD, DLB, or vascular dementia (Gauthier et al., 2006). However, the progression of MCI is also highly variable. Some MCI patients remain with mild cognitive deficits for many years whereas a substantial proportion return to normal cognitive functioning (Ganguli et al., 2004; Gauthier et al., 2006; Fischer et al., 2007). Moreover, several factors may disrupt cognition in elderly individuals, including psychiatric symptoms,medication side effects, respiratory conditions (sleep apneas, chronic obstructive pulmonary disease), and vascular diseases. Consequently, clinicians and researchers should be careful not to directly link MCI to the future development of a neurodegenerative disease or to consider MCI as part of a neurodegenerative disease.

Mild cognitive impairment is a frequent feature of iRBD (Iranzo et al., 2006; Gagnon et al., 2009; Molano et al., 2010). In iRBD patients referred to a sleep disorders center, MCI frequency was estimated at up to 50% compared to 8% in healthy subjects (Gagnon et al., 2009). The main MCI subtype reported was nonamnestic MCI single-domain with predominant attention and executive dysfunctions. No study to date has systematically followed a large cohort of iRBD patients with MCI to determine the risk of developing dementia. However, Molano et al. (2010) followed seven iRBD patients for many years. All patients met MCI criteria and subsequently developed Lewy body disease, which was confirmed by autopsy. This suggests that iRBD patients with MCI are at higher risk for developing DLB.

#### **COGNITIVE DECLINE IN PD ASSOCIATED WITH RBD**

A substantial proportion of PD patients have cognitive impairment, and more than 50% will develop dementia during the course of PD (Aarsland and Kurz, 2010). As mentioned above, RBD is also a frequent feature of PD (Gagnon et al., 2002; Sixel-Döring et al., 2011).  Our team found a higher risk of having MCI in PD with concomitant RBD: MCI was present in 73% of PD patients with RBD compared to 11% of PD patients without RBD and 8% of healthy controls (Gagnon et al., 2009). Moreover, we recently conducted a prospective follow-up study in a cohort of PD patients to assess whether the presence of polysomnographic-confirmed RBD at baseline predicted the future development of dementia according to neurological and neuropsychological assessments (Postuma et al., 2012). The sample comprised 42 PD patients without dementia, including 27 with RBD and 15 without RBD. Over a mean 4-year follow-up, 48% of PD patients with RBD developed dementia, whereas none of PD patients without RBD converted to dementia. Although these results remain to be confirmed in a larger cohort of PD patients, they suggest that the presence of RBD in PD could indicate a more devastating and wide-spread neurodegenerative disease compared to PD patients without RBD symptoms (Postuma et al., 2012).

#### **CONCLUSION**

**Box 1** summarizes a case report of an individual referred to a memory clinic for cognitive decline and subsequently diagnosed with RBD. This case shows the importance of identifying RBD in patients with cognitive impairment. A better understanding of this sleep disorder would enable a deeper grasp of the underlying pathophysiology and diagnosis of synucleinopathies, and would contribute to the development of neuroprotective treatments.

#### **ACKNOWLEDGMENTS**

The authors thank Dr. Jacques Montplaisir and Dr. Ronald Postuma for their contribution to the research program on cognition in REM sleep behavior disorder and Parkinson's disease. Our research is supported by the Canadian Institutes of Health Research and the Fonds de recherche du Québec – Santé.

# **REFERENCES**

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A., Postuma, R. B., Carrier, J., and Montplaisir, J. (2008). Executive dysfunction and memory impairment in idiopathic REM sleep behavior disorder. *Neurology* 70, 1250–1257.

M., Rios Romenets, S., Panisset, M., and Gagnon, J. F. (2012). Rapid eye movement sleep behavior disorder and risk of dementia in Parkinson's disease: a prospective study. *Mov. Disord.* 9, doi:10.1002/mds.24939

Chronic behavioral disorders of human REM sleep: a new category of parasomnia. *Sleep* 9, 293–308.

reveals microstructural changes in the brainstem, substantia nigra, olfactory region, and other brain regions. *Sleep* 33, 767–773.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 19 January 2012; accepted: 25 April 2012; published online: 17 May 2012.*

*Citation: Gagnon JF, Bertrand JA and Génier Marchand D (2012) Cognition in rapid eye movement sleep behavior disorder. Front. Neur. 3:82. doi: 10.3389/fneur.2012.00082*

*This article was submitted to Frontiers in Sleep and Chronobiology, a specialty of Frontiers in Neurology.*

*Copyright © 2012 Gagnon, Bertrand and Génier Marchand. This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits noncommercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.*

# Orbitofrontal gray matter relates to early morning awakening: a neural correlate of insomnia complaints?

#### **Diederick Stoffers 1 , Sarah Moens 1 , Jeroen Benjamins 1 , Marie-José van Tol 2,3, BrendaW. J. H. Penninx 4 , Dick J. Veltman4,5, Nic J. A. Van derWee6,7 and Eus J.W. Van Someren1,8\***

#### **Edited by:**

Julie Carrier, Université de Montréal, Canada

#### **Reviewed by:**

Renate Wehrle, University Clinic Regensburg, Germany Jessica Massicotte-Marquez, Université de Montréal, Canada

#### **\*Correspondence:**

Eus J. W. Van Someren, Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an Institute of the Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA Amsterdam, Netherlands. e-mail<EMAIL_ADDRESS>
Sleep complaints increase profoundly with age; prevalence estimates of insomnia in the elderly reach up to 37%. The three major types of nocturnal complaints are difficulties initiating (DIS) and maintaining (DMS) sleep and early morning awakening (EMA), of which the latter appears most characteristic for aging. The neural correlates associated with these complaints have hardly been investigated, hampering the development of rational treatment and prevention. A recent study on structural brain correlates of insomnia showed that overall severity, but not duration, of insomnia complaints is associated with lower gray matter (GM) density in part of the left orbitofrontal cortex (OFC). Following up on this, we investigated, in an independent sample of people not diagnosed with insomnia, whether individual differences in GM density are associated with differences in DIS, DMS, and EMA. Sixty five healthy participants (mean age = 41 years, range 18–56) filled out questionnaires and underwent structural magnetic resonance imaging. Three compound Z-scores were computed for questionnaire items relating to DIS, DMS, and EMA.Whole-brain voxel-based morphometry was used to investigate their association with GM density. Results show that participants with lower GM density in a region where the left inferior OFC borders the insula report more EMA, but not DIS or DMS. This is the first study to investigate structural brain correlates of specific sleep characteristics that can translate into complaints in insomniacs. The selective association of EMA with orbitofrontal GM density makes our findings particularly relevant to elderly people, where EMA represents the most characteristic complaint. It is hypothesized that low GM density in aforementioned orbitofrontal area affects its role in sensing comfort. An intact ability to evaluate comfort may be crucial to maintain sleep, especially at the end of the night when sleep is vulnerable because homeostatic sleep propensity has dissipated.

**Keywords: insomnia, aging, early morning awakening, orbitofrontal cortex, voxel-based morphometry, structural magnetic resonance imaging**

#### **INTRODUCTION**

With prevalence estimates of 4–11%, chronic insomnia belongs to the most common disorders. The most important risk factor for insomnia is age; estimates of the prevalence in elderly people reach up to about 40% (Foley et al., 1995; Morphy et al., 2007). Individual sleep complaints are much more frequent, with an estimated one third of the population experiencing at least one DSM-IV insomnia symptom (Ohayon, 2002). Three relatively independent major sleep complaints can be discriminated,which can be present with variable severity; difficulties initiating sleep (DIS), difficulties maintaining sleep (DMS), and early morning awakening (EMA; American Psychiatric Association, 1994; American Academy of Sleep Medicine, 2005). Of these three, complaints of EMA most clearly increase with age, while DIS may actually decrease with age; both in clinically diagnosed insomniacs and in the general population of elderly people (Abe and Suzuki, 1985; National Sleep Foundation, 2003a,b). Additional daytime complaints of insomniacs concern non-restorative sleep and problems with daytime functioning.

In spite of the high prevalence and strong impact on wellbeing, quality of life and society (Leger and Bayon, 2010), the brain

**Abbreviations:** DIS, difficulties initiating sleep; DMS, difficulties maintaining sleep; EMA, early morning awakening; GM, gray matter; MRI, magnetic resonance imaging; OFC, orbitofrontal cortex; ROI, region of interest; VBM, voxel-based morphometry.

mechanisms underlying insomnia have hardly been explored. Several psychological models have been proposed, mostly addressing three factors; predisposing personality traits like the tendency to worry, precipitating events like stress, and perpetuating attitudes and practices like misconceptions about required sleep (Spielman, 1986; Perlis et al., 1997). Physiological observations have identified a "hyperaroused" central and autonomic nervous system profile; not only in insomniacs (Bonnet and Arand, 1997; Riemann et al., 2010), but also in individuals that are at risk of developing insomnia (Fernandez-Mendoza et al., 2010). Nevertheless, the brain mechanisms that underlie predisposing traits and a hyperaroused profile have hardly been explored.

Behavioral genetics studies in cohorts of twins and families indicate a considerable heritability of insomnia (Beaulieu-Bonneau et al., 2007) as well as sleep complaints in general (Boomsma et al., 2008). Heritable structural brain characteristics that might be associated with insomnia complaints are thus of particular interest. One of the most heritable structural brain characteristics is the topography of cerebral cortical gray matter (GM) density, particularly so for prefrontal GM where heritability estimates reach up to 95% (Thompson et al., 2001). A recent study demonstrated that elderly insomniacs have a significantly reduced GM density in part of the left orbitofrontal cortex (OFC; Altena et al., 2010). The reduction was found in a region compatible with the border of Brodmann areas 47/12M and 13L of the "orbital network" (Ongur et al., 2003), which is proposed to be essential for the hedonic evaluation of somatosensory input (Kringelbach, 2005), including the evaluation of thermal comfort (Dunn et al., 2010). The observation of compromised thermal comfort-evaluation abilities in elderly insomniacs (Raymann and Van Someren, 2008) supports the possible functional relevance of a reduction in GM density in this relatively small orbitofrontal area. Furthermore, individual differences in GM density in the aforementioned region were strongly correlated with differences in the overall severity of complaints, rendering it the first candidate for a structural brain correlate of insomnia (Altena et al., 2010). In striking contrast, individual differences in GM density were not associated with differences in the duration of insomnia. This dissociation suggests that a low GM density in a region at the border of areas 47/12M and 13L of the OFC is more likely to represent a pre-existing vulnerability to insomnia complaints, rather than a consequence of having experienced a period of insomnia.

The present study aimed to follow up on the promising suggestion of a neural correlate of insomnia by Altena et al. (2010), by investigating, in an independent sample of participants not diagnosed with insomnia, whether individual differences in GM density in aforementioned orbitofrontal region of interest (ROI) are associated with individual differences in the three major types of sleep characteristics: DIS, DMS, and EMA.

#### **MATERIALS AND METHODS**

#### **PARTICIPANTS AND PROCEDURES**

Good quality magnetic resonance imaging (MRI) scans and completed questionnaires could be obtained from 65 self-declared healthy controls participating in the Netherlands Study of Depression and Anxiety (NESDA) neuroimaging study, which is part of a large-scale multisite, longitudinal, observational cohort study on the development and course of anxiety and depression (Penninx et al., 2008). Participants were between 18 and 57 years of age and had never been diagnosed with any of the DSM-IV disorders (American Psychiatric Association, 1994). Further exclusion criteria were the presence or history of major internal or neurological disorder, dependence on or recent abuse (past year) of alcohol and/or drugs, hypertension, any suspicion of current subclinical depression (see van Tol et al., 2010), and contraindications for participating in an MRI study. Diagnoses according to DSM-IV criteria were established using the structured Composite International Diagnostic Interview, lifetime version 2.1 (Robins et al., 1988), assessed by a trained interviewer. **Table 1** provides an overview of participant characteristics, including data on circadian preference (mid-sleep time on free days corrected for sleep debt, computed as described in the supplemental data of; Roenneberg et al., 2004) and overall severity of insomnia complaints (total score on the Women's Health Initiative Insomnia Rating Scale; WHIIRS, see below). The ethical review boards of each center approved this study and all participants provided consent after receiving written information.

#### **ASSESSMENT OF SLEEP COMPLAINTS**

Participants filled out a range of self-rating questionnaires, which included the WHIIRS (Levine et al., 2003a,b) and an early version of the Munich Chronotype Questionnaire (MCTQ, Zavada et al., 2005). Although the WHIIRS has only been validated in women, it contains clearly defined items pertaining to all the core symptoms of insomnia and can thus readily be used to investigate structural brain correlates of specific sleep characteristics in both women and men. Compound *Z*-scores were computed for questionnaire items relating to the three main sleep characteristics: DIS, DMS, and EMA. An estimate of DIS was obtained by calculating the average of standardized ratings on an item of the WHIIRS that asked about trouble falling asleep, and three items of the MCTQ, which ask for the time needed to fall asleep on workdays, on days off, and in general, respectively. An estimate of DMS was obtained by calculating the average of standardized ratings on three items of the WHIIRS that ask about waking up several times at night, about having trouble getting back to sleep, and about restlessness of one's typical night's sleep. An estimate of EMA was obtained by calculating the average of standardized ratings on an item of the WHIIRS asking about waking up earlier than planned, and two items of the MCTQ (Zavada et al., 2005) that ask for the time in the morning one is wide awake on workdays and days off.

#### **Table 1 | Participant characteristics [numbers, ratios, and mean** ± **standard deviation (range)].**

# **MAGNETIC RESONANCE IMAGING ACQUISITION**

Imaging data were acquired using a Philips 3T MRI system (Best, The Netherlands) located at the Leiden University Medical Center (LUMC), Amsterdam Medical Center (AMC) of the University of Amsterdam or the University Medical Center Groningen (UMCG), equipped with a SENSE-8 (LUMC and UMCG) or SENSE-6 (AMC) channel head coil. For each subject, anatomical images were obtained using a sagittal three-dimensional gradient-echo *T*1-weighted sequence (repetition time = 9 ms; echo time = 3.5 ms; matrix = 256 × 256; voxel size = 1 mm isotropic; 170 slices).

#### **VOXEL-BASED MORPHOMETRY-PROCESSING**

Imaging data were analyzed using voxel-based morphometry (VBM: Ashburner and Friston, 2000) with Diffeomorphic Anatomical Registration using Exponentiated Lie algebra (DAR-TEL:Ashburner,2007) in the Statistical ParametricMapping (SPM version 5; http://www.fil.ion.ucl.ac.uk/spm) suite of tools. DAR-TEL is a fully deformable registration and normalization method that is effectively unconstrained by number of degrees of freedom. It has proven good registration accuracy and has been recommended in favor of standard SPM normalization or the SPM unified segmentation approaches for whole-brain and regional analysis without segmenting regions of interest (Yassa and Stark, 2009). Pre-processing of the images has been described in detail previously (van Tol et al., 2010) and included (1) manual reorientation; (2) segmentation; (3) registration, normalization, and modulation (leaving the images in DARTEL template space at a resolution of 1.5 mm isotropic); and (4) smoothing at 8 mm full-width at half-maximum. Total (native) GM volume for each participant was computed by summing all the voxels in their GM image resulting from segmentation (step 2).

#### **VOXEL-BASED MORPHOMETRY ANALYSIS**

Using the peak voxel from the voxel-wise regression analysis against severity of insomnia as reported in Altena et al. (2010), we created a spherical ROI with a 16 mm radius in aforementioned DARTEL template space. First, a matrix describing registration from Montreal Neurological Institute (MNI) space to DARTEL template space was computed using fully affine registration. Next, the obtained transformation matrix was used to transform MNI coordinates (*x*, *y*, *z* = −20, 22, −14) to the present DARTEL template space. Finally, a sphere with a 16 mm radius was centered on these coordinates using the WFU Pickatlas tools (Maldjian et al., 2003, 2004). Visual inspection showed that the resulting ROI predominantly contained parts of the left OFC as well as smaller parts of the left medial frontal, anterior insular and subcallosal cortex, and left caudate. All voxel-wise analyses were performed using the general linear model framework of SPM (Friston et al., 1995). To test for associations with DIS, DMS, and EMA, a whole-brain voxel-wise regression analysis was performed. Age, sex, total GM volume, and imaging site (by means of two dummy variables) were entered as covariates in each analysis. To achieve maximal sensitivity, optimize voxel residual smoothness estimation, and exclude false positives outside GM tissue, voxel-wise analyses were masked using a explicit optimal threshold GM mask created using the Masking toolbox (Ridgway et al., 2009). To preserve optimal

normalization accuracy, we left the normalized, modulated, and smoothed images in DARTEL space. Therefore, coordinates are not equivalent to MNI coordinates. Regions are identified using the detailed brain atlas of Talairach and Tournoux (1988). Additionally, we registered the DARTEL template to MNI space and applied the resulting transformation matrix to the coordinates of the peak voxel in DARTEL space, resulting in an approximation of the location in standard space.

Statistical thresholding was performed roughly analogous to the original publication by van Tol et al. (2010); i.e.,when performing whole-brain voxel-wise analyses we applied different statistical thresholds for analysis *within* and analysis *outside* a ROI. For analyses within the 16 mm radius spherical ROI centered on the peak voxel reported by Altena et al. (2010), we used a two-step approach to reduce the likelihood of Type I errors. Initially, a whole-brain voxel-wise height-threshold of *Z* > 3.09 (*P* < 0.001) and cluster size threshold of 50 contiguous voxels was set. If this initial analysis showed a significant cluster within our ROI, it was followed by a conclusive analysis within our ROI at a threshold of *P* < 0.05, family wise error voxel-corrected. For analysis outside our ROI, a whole-brain threshold of *P* < 0.05 family wise error cluster-corrected was set.

To gain more insight into effect size as well as for illustrative purposes, we computed mean GM density within significant clusters by averaging of the GM probabilities for each voxel in a cluster. To investigate the amount of explained variance in mean GM density within a significant cluster by DIS, DMS, and EMA, multiple regression analysis was performed twice; once with and once without DIS, DMS, or EMA, using age, sex, total GM volume, and imaging site (by means of two dummy variables) as predictors of no interest in each analysis.

#### **ANALYSIS OF SLEEP MEASURES**

The general linear model framework of Statistical Package for the Social Sciences (SPSS version 17.0, Chicago, IL, USA) was used for the analyses of compound *Z*-scores. Reliability (internal consistency) of the DIS, DMS, and EMA measures was evaluated by calculating Cronbach's alpha.

#### **MISSING DATA**

In case of missing values on any of the questionnaire items in a participant, compound *Z*-scores were computed over the remaining items for that specific sleep complaint. For DMS, compound *Z*scores could not be computed in three participants because scores were not available for any of the three items that make up DMS, leaving 62 participants for the analysis of the association of GM density and DMS.

# **RESULTS**

#### **VOXEL-BASED MORPHOMETRY ANALYSIS**

Voxel-wise analysis showed that the EMA compound *Z*-score significantly predicted GM density in a 66 voxel, 0.223 cc cluster at *P* < 0.001 uncorrected. The complete cluster was located within our 16 mm radius spherical ROI and the peak voxel (approximate MNI coordinates = −30, 13, −13) survived correction for multiple comparisons (*P* = 0.029, family wise error voxel-corrected). An auxiliary analysis, using mid-sleep time on free days (corrected

**FIGURE 1 | Effects of voxel-based regressions.** A cluster of voxels is shown in which GM probability was a significant predictor for the early morning awakening compound Z -score, overlaid on all coronal **(A)**, axial **(B)**, and sagittal **(C)** 1.5 mm slices that contain the cluster from the DARTEL template,

for sleep debt) instead of EMA to predict GM density, showed no significant voxels within our ROI at *P* < 0.001 uncorrected. The cluster for EMA encompassed the region where the left inferior OFC borders with the most anterior aspect of the insular cortex (**Figure 1**). The association of EMA with the average GM density within this cluster (i.e., an average of the GM probabilities for each voxel in the cluster) is visualized in the scatterplot of **Figure 2**; EMA explained an additional 6% of variance on top of the other predictors age, sex, total GM volume, and imaging site. Voxel-wise analysis at *P* < 0.001 uncorrected within our ROI showed no significant association of GM density with either DIS or DMS.

No effects were observed when testing at the conservative threshold of *P* < 0.05 family wise error cluster-corrected. At the liberal threshold of *P* < 0.001 uncorrected, only a single cluster was found outside our ROI for EMA (in the posterior division of the right superior temporal gyrus), which speaks to the specificity of the observed effect in our voxel-wise ROI analysis.

#### **SLEEP COMPLAINTS**

Although Cronbach's alpha underestimates reliability when computed for a limited number of items, it demonstrated at least a minimally acceptable to even good internal consistency for the compound *Z*-scores when compared to commonly used standards for larger item pools (Cronbach's alpha DIS: α = 0.85, DMS: α = 0.69, and EMA: α = 0.59). As to independence of the three compound scores, correlation analysis demonstrated that DIS was relatively independent of both DMS [*r*(62) = 0.20, *P* = 0.12] and EMA [*r*(65) = 0.06, *P* = 0.66], while DMS and EMA showed a significant correlation [*r*(62) = 0.52, *P* < 0.001].

according to neurological convention (left = left). The blue bar in the localizer on the left side of the panels represents the slab of which slices are shown on the right. Note that the cluster overlaps the border of the inferior part of the orbitofrontal cortex and the most anterior aspect of the insular cortex.

## **DISCUSSION**

We aimed to investigate whether individual differences in GM density in a ROI compatible with the border of Brodmann areas 13L and 47/12M of the left OFC are associated with individual differences in the self-reported degree by which they experienced DIS, DMS, and EMA. This ROI was chosen because previous work from our group showed a strong correlation between GM density in this area and the severity of complaints in people diagnosed with chronic primary insomnia (Altena et al., 2010), thus indicating it to be a neural correlate of insomnia severity. The present study also supports an involvement of this area in sleep characteristics of people that have not been diagnosed with insomnia. Moreover, findings indicate that the association is specific for EMA; it was absent for difficulties initiating and maintaining sleep. This selectivity makes our findings particularly relevant to elderly people; EMA represents the complaint that most characteristically increases with age (Abe and Suzuki, 1985) and prefrontal GM volume is especially vulnerable to age-related decline (Raz et al., 1997, 2005; Good et al., 2001).

The present study has a considerable limitation with respect to the quantification of DIS,DMS, and EMA.We calculated summary *Z*-scores over relevant items derived from different self-rating questionnaires designed for different purposes, for which data was available in the NESDA study. DIS was computed over four items, while DMS and EMA were both calculated over three items. With very few items, it is unlikely to obtain high alphas unless the items are nearly identical to one another. Considering the small number of items used and their inevitable heterogeneity, even reliabilities above four are often considered reasonable. We can at present only conclude that orbitofrontal GM density shows a significant

negative association with earlier awakening in the morning, which may or may not result from different underlying causes and may not be independent of DMS. It would be interesting to investigate, using large item pools, whether DIS, DMS, and EMA are each unidimensional, or rather multidimensional, constructs. Presently,we are not aware of sleep questionnaires that evaluated and validated unidimensional multi-item subscales for DIS, DMS, and EMA.

For the current study, imaging and questionnaire data was available for a sample of individuals spanning a wide age range (18–56 years). As both sleep complaints and brain atrophy increase with age, the observed correlations could in theory merely be due to common age affects. However, both native total GM volume and age were included as confound regressors in all our analyses, thus correcting for intra-individual differences in global brain atrophy as well as age-related changes in local GM volume. Consequently, we are confident that common aging effects cannot explain the observed relation between GM density and EMA.

The orbitofrontal area we focused on in the present study may represent a "hot-spot" for the hedonic evaluation of somatosensory input since it activates with pleasant stimuli (Kringelbach, 2005). It is important to note that there does not appear to be a unidimensional monotonic deactivation of this area from pleasure to aversion; the latter activating a more lateral orbitofrontal area instead (O'Doherty et al., 2001). A recent human brain imaging study elegantly showed that an orbitofrontal region compatible with the border of areas 13L and 47/12M is key to the evaluation of thermal comfort, irrespective of the evaluation of warmth or coolness itself (Dunn et al., 2010). This finding is of interest, because insomniacs have a compromised ability to judge thermal comfort (Raymann and Van Someren, 2008) and even small deviations

from comfortable temperature have a strong impact on sleep (Van Someren, 2004, 2006; Raymann et al., 2008). It is tempting to suggest that a low GM volume in the orbitofrontal region at the border of areas 13L and 47/12M could predispose people to suboptimal sensing of comfort. An intact ability to evaluate comfort may be crucial to maintain sleep especially at the end of the night, when sleep is vulnerable because homeostatic sleep propensity has dissipated.

The cluster where the present study found EMA to account for 6% of the interindividual variability in GM density is not limited to the OFC, but extends toward the border of the insula. The insula is an interesting area with respect to the slow oscillations that characterize deep non-REM sleep. High-density sleep-EEG studies have demonstrated that slow oscillations tend to travel over the cortex mostly in an anteroposterior direction (Massimini et al., 2004). Although they can emerge at any part of the cortex, source estimates showed they are most likely to first appear in the insula and then propagate along the anteroposterior axis through the brain midline (Murphy et al., 2009). It is tempting to speculate that a relatively low GM volume in part of the insula may predispose people to a less firm generation of slow oscillations. Slight interindividual differences in ability to ignite slow oscillations may become noticeable only at the end of the night, when sleep is vulnerable because homeostatic sleep propensity has dissipated.

From the perspective of present-day models on sleep regulation, involvement of the OFC and comfort sensing in sleep EMA may be surprising at first glance. The consensus model of sleep-wake regulation consists of a circadian process and a homeostatic process and is aptly referred to as the two-process model of sleep regulation (Borbely, 1982; Daan et al., 1984; Dijk and Czeisler, 1995). The circadian process refers to the timing of sleep and wakefulness driven by the clock of the brain, located in the hypothalamic suprachiasmatic nucleus (Mistlberger, 2005). The homeostatic component refers to the hourglass of the brain, that keeps track of the time we are awake and gradually increases the pressure for sleep. This pressure dissipates as soon as sleep sets in. The decrease follows an exponential trajectory, so that most of the sleep pressure is dissipated within a few hours of sleep (Achermann et al., 1993; Leemburg et al., 2010; Van Someren, 2010). The neurobiological mechanisms underlying this hourglass mechanism are not as well characterized as the mechanisms of the clock, but are likely to involve adenosine (Porkka-Heiskanen et al., 1997), cytokines (Krueger et al., 2011), and an increase in synaptic density during wakefulness (Tononi and Cirelli, 2006).