Elsevier

Sleep Medicine

Volume 34, June 2017, Pages 179-192
Sleep Medicine

Original Article
Sleep stage distribution in persons with mild traumatic brain injury: a polysomnographic study according to American Academy of Sleep Medicine standards

https://doi.org/10.1016/j.sleep.2017.02.021Get rights and content

Highlights

  • Disruption of sleep stage regulation is detrimental to mental and bodily functions.

  • Sleep stage regulation is disrupted in both men and women with mTBI.

  • mTBI patients have longer nocturnal wakefulness vs. matched normative data.

  • Increased wakefulness in mTBI is due to shortened N2 and REM sleep.

  • These changes were related to patients' reports of insomnia, physical, and emotional symptoms.

Abstract

Objective and background

Sleep stage disruption in persons with mild traumatic brain injury (mTBI) has received little research attention. We examined deviations in sleep stage distribution in persons with mTBI relative to population age- and sex-specific normative data and the relationships between such deviations and brain injury-related, medical/psychiatric, and extrinsic factors.

Patients and methods

We conducted a cross-sectional polysomnographic investigation in 40 participants diagnosed with mTBI (mean age 47.54 ± 11.30 years; 56% males).

Measurements

At the time of investigation, participants underwent comprehensive clinical and neuroimaging examinations and one full-night polysomnographic study. We used the 2012 American Academy of Sleep Medicine recommendations for recording, scoring, and summarizing sleep stages. We compared participants' sleep stage data with normative data stratified by age and sex to yield z-scores for deviations from available population norms and then employed stepwise multiple regression analyses to determine the factors associated with the identified significant deviations.

Results

In patients with mTBI, the mean duration of nocturnal wakefulness was higher and consolidated sleep stage N2 and REM were lower than normal (p < 0.0001, p = 0.018, and p = 0.010, respectively). In multivariate regression analysis, several covariates accounted for the variance in the relative changes in sleep stage duration. No sex differences were observed in the mean proportion of non-REM or REM sleep.

Conclusions

We observed longer relative nocturnal wakefulness and shorter relative N2 and REM sleep in patients with mTBI, and these outcomes were associated with potentially modifiable variables. Addressing disruptions in sleep architecture in patients with mTBI could improve their health status.

Introduction

Traumatic brain injury (TBI), defined as “alteration in brain function or other evidence of brain pathology caused by an external force,” is a significant clinical and public health problem, regardless of injury severity [1], [2], [3]. For example, the prevalence of mild TBI (mTBI) in Ontario, Canada [4], is between 493/100,000 and 653/100,000, depending on whether the diagnosis was made by a primary care physician or a secondary reviewer [5]. Although many people with mTBI recover fully, at least 15% continue to experience long-term and possibly permanent limitations in physical, cognitive, and emotional functioning; this causes consumption of a disproportionate amount of healthcare services [6], [7], [8]. Notably, despite decades of research, there remain gaps in our understanding of the mechanisms underlying delayed recovery and persistent post-mTBI symptoms. In fact, the duration and severity of postinjury symptoms following mTBI are surprising, given the limited or lack of pathoanatomical findings in the brain [9], [10].

One of the most commonly reported symptoms post-TBI is sleep dysfunction, including the inability to fall asleep or maintain uninterrupted sleep, behavioral manifestations during sleep, and dissatisfaction with sleep quality [11], [12]. Studies of sleep architecture in humans and animals have identified the role of the cerebral cortex, thalamus, and multiple brainstem nuclei in regulating sleep stage distribution [13], [14], [15]. Therefore, sleep stage distribution might be a marker of organic dysfunction acquired through or exacerbated by mTBI. However, any persistent changes in sleep stage distribution post TBI could also be a consequence of other factors (maturational brain evolution, stress, somatic, psychiatric, and primary sleep disorders, drugs, etc.) [16], [17]. This raises the question of whether sleep deviations in mTBI are indicative of organic brain damage or manifestations of psychosocial factors.

Normal sleep architecture has three clearly defined major vigilance states: wakefulness [ie, wake after sleep onset (WASO)], non-rapid eye movement (NREM) sleep (divided into stages 1–4 or N1–N3 in the new classification), and rapid eye movement (REM) sleep [18]. The proportion of time spent in each stage of sleep is both age- and sex-dependent but is fairly consistent in healthy adults [19]; thus, population-based normative data for sleep stage distribution by age and sex have been established over decades of sleep research [20].

Correlative methodologies are often used to study sleep stage distribution across neural, behavioral, and environmental factors because of their relative simplicity. There are well-known factors that can modify sleep stage distribution in healthy persons, including prior sleep history, drugs, primary sleep disorders (eg, narcolepsy and sleep apnea), and other nonsleep internal problems (pain, psychosocial distress, etc.). In addition, there are also well-known environmental factors (ie, temperature, noise, light, etc.) that can affect sleep architecture [19], [21], [22], [23]. However, it is not always obvious which of these factors are in play, either in healthy people or in those with mTBI. Furthermore, while all four polysomnography (PSG) studies of patients with mTBI have documented changes in sleep stage distribution, their results have been inconsistent [24], [25], [26], [27]. Khoury et al. observed no differences in sleep architecture (N1, N2, N3, or REM) between young mTBI participants with acute pain after trauma when compared to healthy controls, although the mTBI participants were significantly older (p = 0.01) [24]. Gosselin et al. found no differences in sleep architecture between 10 young concussed and 11 nonconcussed athletes; however, between-group differences were found in REM and NREM quantitative EEG variables [25]. Williams et al. observed no significant differences in sleep stages between nine participants with mTBI (18–26 years old) and age-matched controls, although mTBI participants had lower sleep efficiency (SE), shorter REM, and greater sleep onset latency than the controls [26]. In contrast, Schreiber et al. reported altered sleep stage distribution in their sample of middle-aged persons with mTBI, with significantly higher proportions of N2 and significantly lower REM sleep than age- and sex-matched controls [27]. These inconsistencies could be due to differences in methodology, sample size, or selection of patients (eg, unmatched controls or small samples). In addition, Schreiber et al. concluded that different structures involved in normal sleep (brainstem, basal ganglia, and hypothalamus) might be affected in mTBI [27], which suggests that the side and localization of the external blow to the head may play a role in the variability of the data.

From these previous studies, we hypothesized that (1) the proportion of time spent in each sleep stage by persons with mTBI would deviate from age- and sex-specific norms, (2) these deviations would be driven by changes in the nocturnal wakefulness, transitional sleep stage (N1), and REM sleep, and (3) markers of primary sleep disorders would be associated with deviations in sleep stage distribution. To test this, we evaluated sleep stage distribution in a sample of middle-aged persons (males and females) with persistent symptoms post mTBI. In contrast to previous research, we did not select a sample free of other disorders known to disturb sleep architecture but rather investigated sleep stages and their deviations from established age- and sex-specific norms, taking into account all currently known factors that can disturb the sleep architecture (Fig. 1). By comparing data from our sample population to published values for normal sleep stage distribution matched by age and sex, we sought to (1) assess the magnitude of changes in sleep stage distribution, (2) examine associations between factors known to modify sleep stage distribution and changes related to mTBI, and (3) describe any potential sex differences when compared with population norms.

Section snippets

Methods and materials

The Research Ethics Board at the Toronto Rehabilitation Institute-University Health Network (UHN) and the University of Toronto Research Ethics Board approved the study. All participants provided written informed consent to use their information.

Z-scores and sleep stage deviations from age- and sex-specific norms

Individual displays of z-scores for absolute values are shown in Supplementary file 1. We employed the Mann–Whitney U test separately for males and females to determine statistically significant group z-scores (see Fig. 3) for each stage of sleep. The mTBI group exhibited significantly increased nocturnal wakefulness (ie, WASO) for males and females vs. population norms (12.69 ± 14.13, p < 0.0001 and 12.15 ± 9.49, p < 0.0001, respectively). The mTBI group also exhibited less time in N2

Discussion

Most previous studies on mTBI that employed PSG identified increased light sleep and reduced REM sleep in patient populations compared to healthy age- and sex-matched controls [24], [25], [43], [44], [45], [46]. Our study's methodological novelty lies in the comparison of PSG-derived data for each sleep stage between each individual patient with mTBI/concussion and the corresponding normative population-based group of similar age and sex. Although our results generally confirmed those of

Funding

Our study had no external funding source. The first author was supported by the 2011 Neurotrauma Foundation Summer Scholarship, the 2011/2012 Mitacs Accelerate Graduate Internship, the 2013/2015 Frederick Banting and Charles Best Doctoral Research Award from the Canadian Institutes of Health Research (#290950), and the postdoctoral research fellowship from the University of Toronto and the Alzheimer's Association (AARF-16-442937). Angela Colantonio was supported by the Canadian Institutes for

Acknowledgments

We gratefully acknowledge Mr. Kevin McCurley for his help with the data management, Dr. Liouda Voronovicth for scoring the PSG data, and personnel of the Sleep Research Unit of the Toronto Western Hospital-UHN for accepting/accommodating research participants on a short notice.

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