Journal of Affective Disorders 158 (2014) 11–18

Contents lists available at ScienceDirect

Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad

Review

Polysomnographic features of early-onset depression: A meta-analysis Jura L.S. Augustinavicius a,b, Anosha Zanjani e, Konstantine K. Zakzanis d,n, Colin M. Shapiro a,b,c a

Department of Cell and Systems Biology, University of Toronto, Canada Youthdale Child and Adolescent Sleep Centre, Toronto, Canada c Department of Psychiatry, University of Toronto, Canada d Department of Psychology, University of Toronto Scarborough, 1265 Military Trail, Toronto, Ontario, Canada M1C 1A4 e Centre for Addiction and Mental Health, Toronto, Canada b

art ic l e i nf o

a b s t r a c t

Article history: Received 23 September 2013 Received in revised form 22 October 2013 Accepted 1 December 2013 Available online 9 January 2014

Background: Undiagnosed major depressive disorder (MDD) is associated with increased morbidity in children and adolescents. This study evaluated features of sleep macro- and microarchitecture assessed by polysomnography (PSG) as diagnostic markers for MDD in children and adolescents. Methods: MEDLINE, PSYCINFO, EMBASE and PUBMED were searched from their availability dates to March 1st, 2013. The literature search identified 932 abstracts of which 51 studies were retrieved and 28 were included in the analysis. Study design, features of sleep macro- and microarchitecture, demographic and clinical characteristics of study groups were extracted for each study. Results: There were modest differences on sleep macroarchitecture between children and adolescents with MDD and healthy controls. The most robust difference was found in sleep latency, 31% of adolescents with MDD had increased sleep latency. Age, suicidal ideation, suicidal behavior, and psychiatric comorbidities were significant predictors of sleep macroarchitecture. Modest differences were found for sleep microarchitecture, intrahemispheric and interhemispheric temporal coherence was decreased in a third of patients with MDD. Age was a significant predictor of sleep microarchitecture. Limitations: This meta-analysis is limited by the small number of studies on sleep macroarchitecture in children with MDD and studies on sleep microarchitecture overall and by the heterogeneity in methodology between studies. Conclusions: This synthetic review of the existing literature is among the largest to quantitatively assess impaired sleep as a diagnostic marker for MDD in children and adolescents. Knowledge of sleep macroand microarchitecture in early-onset MDD may aid the clinician in developing a treatment strategy for MDD-related sleep symptoms in a subset of patients. & 2014 Elsevier B.V. All rights reserved.

Keywords: Major depressive disorder Sleep Polysomnography Sleep electroencephalography Depression Pediatrics

Contents 1. 2.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Search strategy and study selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Data extraction and synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. Statistical analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Sleep macroarchitecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Sleep microarchitecture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Role of funding source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conflict of interest. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

n

Corresponding author. Tel.: þ 1 416 726 4323; fax: þ 1 416 287 7642. E-mail address: [email protected] (K.K. Zakzanis).

0165-0327/$ - see front matter & 2014 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jad.2013.12.009

12 12 12 13 13 13 14 15 16 17 17 17

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Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Appendix A. Supporting information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

1. Introduction Major depressive disorder (MDD) is the leading cause of disability worldwide, often recurring over the lifespan (WHO, 2012). The cumulative prevalence of MDD is 12% in females and 7% in males by the age of 16 (Costello et al., 2003), underscoring the need for advances in early detection of the illness. Structured psychiatric interviews used for MDD diagnosis based on criteria set out by the Diagnostic and Statistical Manual of Mental Disorders (DSM) often underestimate the degree of impairment and result in under-diagnosis in children and adolescents (Angold et al., 1999). The relationship between sleep and depression is bidirectional, where sleep disturbances are one of the core features of depressive illness (American Psychiatric Association, 2000) and persistent insomnia and sleep disturbance increase the risk of major depressive episode (MDE) onset and relapse (Wulff et al., 2010). While polysomnographic (PSG) studies in adults with depression have demonstrated significant differences from controls on several sleep parameters (Pillai et al., 2011), the results from investigations in pediatric samples have been equivocal (Ivanenko et al., 2005; Rao, 2011). These studies have traditionally examined sleep macroarchitecture, which describes the latencies and durations of sleep stages. Disturbances in duration of PSG parameters, such as total sleep time (Emslie et al., 1994; Kutcher et al., 1992), sleep efficiency (Appelboom-Fondu et al., 1988; Goetz et al., 1987; Rao and Poland, 2008), non-rapid eye movement (NREM) sleep (stages 1–4) (Armitage et al., 2001; Goetz et al., 2001), and rapid eye movement (REM) sleep (Emslie et al., 1990), as well as the frequency of REMs (REM density) (Dahl et al., 1990; Emslie et al., 2001; Lahmeyer et al., 1983), have been characterized in children and adolescents with MDD. Latencies, such as the time to sleep onset and time to the first stage of REM have been described in narrative reviews as robust features of pediatric sleep macroarchitecture in MDD (Ivanenko et al., 2005; Rao, 2011). These positive findings have coincided with divergent negative results on many of the same features of sleep macroarchitecture (Goetz et al., 1985; Puig-Antich et al., 1982; Young et al., 1982). Alterations in sleep microarchitecture, the detailed analysis of frequencies detected by PSG during sleep such as high frequency beta (416 Hz), and lower frequencies such as theta (6–4 Hz) and delta (o4 Hz) (Armitage et al., 1992; Borbely et al., 1984), have held promise as sleep markers for depression (Armitage, 2007). The asynchrony of PSG-derived high and low frequencies within and between hemispheres is thought to represent a dysregulation of neural activation and arousal systems (Armitage et al., 2000a) and has successfully differentiated children and adolescents with depression from controls (Armitage et al., 2000a, 2006). To date, this measure of asynchrony, termed temporal coherence, is the most widely described feature of sleep microarchitecture in MDD (Armitage et al., 1999, 1993; Fulton et al., 2000; Knott et al., 2001). Decreased coherence between beta and delta frequency bands within the right and left hemispheres, and beta coherence between the left and right hemispheres has been reported in children and adolescents with MDD (Armitage et al., 2000a). Measures of temporal coherence have been investigated in at-risk adolescents (Morehouse et al., 2002) and have accurately predicted MDD severity and time to recovery in early-onset MDD (Armitage et al., 2002). To address the question of PSG-derived sleep disturbance as a marker for early-onset depression, we sought to quantitatively

review evidence from the sleep macroarchitecture literature. The primary objective of this study was to perform a metaanalysis to assess features of sleep macroarchitecture as markers for MDD in children and adolescents. We hypothesized that children and adolescents with MDD would differ most from their healthy peers on measures of sleep latency and REM latency. Our secondary objective was to evaluate the sensitivity of sleep microarchitecture parameters in detecting MDD through temporal coherence to test our hypothesis that these measures would perform as better markers for MDD compared to features of sleep macroarchitecture. A final aim was to identify any relationships between patient characteristics such as age, and associated features of MDD, such as the number of previous MDEs, suicidality, psychiatric comorbidities, and sleep parameters.

2. Methods 2.1. Search strategy and study selection The Meta-analysis of Observational Studies in Epidemiology guidelines for meta-analyses of observational studies guided our review in this study (Stroup et al., 2000). MEDLINE, PSYCINFO, EMBASE and PUBMED were searched from their dates of origin to March 1st, 2013. A secondary search involved checking the reference sections of relevant review papers and primary studies for articles that may have been missed in the database search. The literature search was restricted to peer-reviewed research studies describing features of sleep macroarchitecture or microarchitecture using overnight PSG in children and adolescents with MDD. The following search terms were used: major depressive disorder OR depression OR MDD AND children OR adolescents OR teenagers OR youth AND polysomomnograph OR polysomnography OR PSG OR REM OR slow wave sleep OR slow wave activity OR NREM sleep OR delta sleep OR sleep macron OR sleep macroarchitecture OR sleep micron OR sleep microarchitecture OR coherence OR temporal coherence. Studies were included for macroarchitecture analysis if they compared features of sleep macroarchitecture between depressed and healthy children and/or adolescents. Studies were included for microarchitecture analysis if they compared temporal coherence during sleep between depressed children and/or adolescents and a comparison group, before and after the onset of depression, or during a depressive episode and during remission of a depressive episode. Only articles in English were reviewed. Study statistics had to be convertible to effect size (e.g., means, standard deviations, F, t). Children were 12 years or younger and adolescents were 13–21 years. Formal MDD diagnosis using DSM criteria was necessary for inclusion. Only studies that evaluated sleep features using PSG were included. Exclusion criteria for both macro- or microarchitecture analysis were use of the same study cohort in multiple studies, case studies, the diagnoses of Bipolar I or II Disorder, Autism Spectrum Disorders, or substance use disorders. Studies that administered drugs, or used intravenous catheters or other techniques for obtaining biological measurements during PSG were excluded unless baseline PSG results were available for at least one night using standard sleep protocols.

J.L.S. Augustinavicius et al. / Journal of Affective Disorders 158 (2014) 11–18

2.2. Data extraction and synthesis Data extracted from each study were author, year of publication, study design, any moderator variables reported, and sleep outcome variables. Effect sizes were calculated for the macroarchitecture features of sleep that are commonly clinically evaluated based on PSG recording. Macroarchitecture features included total sleep time, sleep latency in minutes, sleep efficiency (calculated as total sleep time divided by total time in bed), awake time, stage 1, stage 2, stage 3, stage 4, slow wave sleep (stages 3 and 4), REM latency in minutes, REM sleep, and REM density. Data were extracted for period length of macroarchitecture features as either a percentage of total sleep time or in minutes. Effect sizes for microarchitecture features were calculated for measures of temporal coherence given that these measures are the most consistently described among a limited number of studies that have investigated sleep microarchitecture. Microarchitecture features included intrahemispheric coherence between beta and delta in the right hemisphere (BDRCOH), beta and delta in the left hemisphere (BDLCOH), theta and delta in the right hemisphere (TDRCOH), theta and delta in the left hemisphere (TDLCOH), and interhemispheric coherence of beta between the right and left hemispheres (BCOH), and theta between the right and left hemispheres (TCOH). Effect sizes were calculated for each macroarchitecture and microarchitecture variable for pooled samples (i.e. children and adolescents) and for children and adolescents as sub-groups. Macroarchitecture moderator variables included the age of patients with MDD, number of previous MDD episodes, mean episode length, suicidal ideation, suicidal behavior, and comorbidities of dysthymia, anxiety, ADHD, and other externalizing disorders. Only the age of patients with MDD was used as a moderator variable for meta-regression of microarchitecture features.

13

sizes that are not accounted for by sampling error alone. Moderator variables were recorded and correlated with the effect size in order to tease out relationships that may influence the magnitude of the effect. A meta-regression was performed using simple linear regression of each moderator variable on the mean effect size of each sleep feature. Means are presented in the text7standard deviation unless otherwise specified. The heterogeneity in effect sizes was measured using Q and I2 (Higgins et al., 2003). A significant Q indicates that there are substantive differences between studies contributing to the pooled effect size. I2 was used as a second measure of heterogeneity of effect size representing within study heterogeneity expressed as a percentage of the total variation across studies. The effect sizes were transformed into a non-overlap percentage using Cohen0 s (1988) idealized distributions, which were further transformed into an overlap percentage (OL%) to articulate the meaningfulness of the effect size (Zakzanis, 1998, 2001). In the present context, the OL% statistic represents the degree of overlap between participants in the patient group (i.e. individuals with depression) and participants in the control group (i.e. individuals without depression). Publication bias was assessed by detecting asymmetry in funnel plots through funnel plot analysis (Borenstein et al., 2009). Publication bias was also assessed by the fail-safe N formula (Orwin, 1983) that provides an index of the number of studies that are needed to theoretically yield a non-significant effect (i.e., d¼ 0.1). In order to weight effect size calculations according to sample size, the method by Hedges and Olkin (1985) was used. This method ensures that the weight given to the effect size for one study is inversely proportional to the variance of each other study and proportional to the sample size of each individual study (Hedges and Olkin, 1985).

2.3. Statistical analyses 3. Results

Identification

934 records identified by database search strategy

1 record identified through other sources

Screening

935 abstracts reviewed

884 abstracts excluded

Eligibility

There were 28 studies included in the meta-analysis (Fig. 1). Of these, 24 studies reported on macroarchitectural features, four reported on microarchitectural features, and one reported on both macro- and microarchitectural features of sleep. All studies used a between subjects design. Effect sizes and demographic characteristics of each study stratified by macro- and microarchitecture features of sleep are displayed in Supplementary Table 1. The number of effect sizes exceeds the number of studies because some studies contributed effect sizes for children and for adolescents.

51 full-text articles reviewed for eligibility

Included

Meta-analytic techniques were used to perform this review of the literature (Cooper and Hedges, 1994; Hedges and Olkin, 1985; Rosenthal, 1991, 1995). The magnitude of difference between groups was articulated with the effect size estimate Cohen0 s d. Cohen0 s d reflects the degree to which the dependent variable is present in the sample group or the degree to which the null-hypothesis is false (Cohen, 1988). A random effects model was used to calculate d based on the means and standard deviations for each group from individual studies (Lipsey and Wilson, 1993). The random effects model accounts for the heterogeneity of factors that result in differing effect

28 studies included in meta-analysis

23 full-text articles excluded 5 Non-DSM instrument used for assessment of MDD 4 Lack of healthy control group 1 Adults included in sample 2 Drug administration during study period 1 Case report 5 Used same cohort as other studies 3 Full-text article not in English 2 Insufficient data

Fig. 1. Data collection process and study exclusion for meta-analysis. Source: Figure adapted from Ross et al. (2013) and Moher et al. (2009).

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3.1. Sleep macroarchitecture The meta-analysis of 24 studies (Arana-Lechuga et al., 2008; Armitage et al., 2001, 2000a; Bertocci et al., 2005; Dahl et al., 1990, 1991, 1996; Delvenne et al., 1992; Emslie et al., 1987, 1990, 1994; Forbes et al., 2008; Goetz et al., 1987; Khan and Todd, 1990; Kutcher et al., 1992; Lahmeyer et al., 1983; Lopez et al., 2010; McCracken et al., 1997; Puig-Antich et al., 1983; Rao and Poland, 2008; Riemann et al., 1995; Robert et al., 2006; Wojnar et al., 2010; Young et al., 1982) reporting on macroarchitectural features of sleep included 810 patients with MDD (48% males) and 603 healthy controls (46% males). The MDD patients had a mean age of 13.46 72.34 and a Tanner score of 3.21 70.59. The control

group had a mean age of 13.387 2.52 and a Tanner score of 3.347 0.71. Suicidal ideation and suicidal behaviors were reported in 27.36 711.47% and 16.47 717.77% of patients, respectively. MDD patients had a number of comorbid diagnoses including dysthymia (19.31 7 17.06%), anxiety disorder (21.517 18.57%), ADHD (16.02 7 16.25%), and other externalizing disorders (13.66 712.96%). Mean effect sizes for features of sleep macroarchitectural are displayed in Table 1. Total sleep time was significantly shorter in 26% of depressed children compared to controls. Sleep onset latency was longer in both children and adolescents with MDD, 27% of depressed children and 31% of adolescents could be differentiated from controls based on this feature. Sleep efficiency was decreased in 28% of adolescents and in 25% of children and

Table 1 Effect sizes for features of sleep macroarchitecture. pQ

I2

93.43 11.30 39.14

o 0.01 0.13 o 0.01

76.45 38.06 74.45

73 70 69

16.31 4.72 6.84

0.84 0.69 0.87

o 0.01 0.36 o 0.01

75 88 72

34.34 11.69 15.38

0.02 0.07 0.17

44.68 48.69 28.49

112 0 50

 0.14/0.21  0.21/0.29  0.21/0.39

0.72 0.74 0.54

98 97 94

41.98 7.99 27.43

o 0.01 0.33 o 0.01

49.97 12.40 59.90

0 0 0

 0.14 (0.10)  0.14 (0.13)  0.15 (0.16)

 0.34/0.06  0.39/0.11  0.47/0.17

0.18 0.29 0.37

91 91 89

57.23 8.21 30.78

o 0.01 0.32 o 0.01

63.31 14.71 64.26

2 0 0

18 6 10

 0.04 (0.13) 0.19 (0.20)  0.07 (0.19)

 0.30/0.21  0.20/0.58  0.43/0.30

0.74 0.34 0.71

97 86 96

51.33 9.66 24.85

o 0.01 0.09 o 0.01

66.99 48.22 63.78

0 0 0

Total sample Children Adolescents

18 6 10

 0.06 (0.11)  0.02 (0.14)  0.10 (0.18)

 0.27/0.15  0.29/0.25  0.45/0.25

0.57 0.89 0.57

96 99 92

34.98 4.63 22.61

o 0.01 0.46 0.01

51.40 0 60.19

0 0 0

Slow wave sleep

Total sample Children Adolescents

11 3 5

 0.14 (0.15)  0.31 (0.65) 0.10 (0.19)

 0.43/0.15  1.58/0.95  0.26/0.47

0.35 0.63 0.58

89 78 92

35.74 20.25 7.39

o 0.01 o 0.01 0.12

72.02 90.13 45.89

0 0 0

REM

Total sample Children Adolescents

23 8 13

0.28nn (0.11) 0.59nn (0.22) 0.07 (0.13)

0.07/0.48 0.15/1.03  0.19/0.32

0.01 0.01 0.61

80 62 96

62.10 22.66 22.32

o 0.01 o 0.01 0.03

64.57 69.1 46.24

79 36 0

REM latency

Total sample Children Adolescents

23 8 13

 0.31nn (0.11)  0.32 (0.22)  0.32n (0.16)

o 0.01 0.14 0.05

77 76 78

65.69 21.83 34.32

o 0.01 o 0.01 o 0.01

66.51 67.94 65.03

103 0 23

REM density

Total sample Children Adolescents

16 5 8

0.56nn (0.20) 0.34 (0.50) 0.47n (0.22)

0.17/0.95  0.65/1.32 0.05/0.90

0.01 0.50 0.03

63 75 68

126.99 45.48 27.03

o 0.01 o 0.01 o 0.01

88.19 91.21 74.10

110 0 25

Awake time

Total sample Children Adolescents

16 5 10

0.09 (0.14)  0.31 (0.19) 0.23 (0.11)n

 0.19/0.36  0.67/0.06 0.02/0.44

0.54 0.06 0.03

95 78 83

49.04 4.71 7.66

o 0.01 0.32 0.57

69.41 15.01 0

0 0 0

Sleep feature

Study group

N

Mean d (SE)

95% CI

Total sleep time

Total sample Children Adolescents

23 8 11

 0.04 (0.12) 0.35n (0.15)  0.08 (0.19)

0.21/0.28 0.05/0.65  0.46/0.30

0.76 0.02 0.67

98 74 95

Sleep latency

Total sample Children Adolescents

24 8 13

0.39nn (0.06) 0.43nn (0.12) 0.43nn (0.09)

0.28/0.50 0.20/0.66 0.25/0.60

o 0.01 o 0.01 o 0.01

Sleep efficiency

Total sample Children Adolescents

20 7 12

0.34nn (0.10)  0.16 (0.17)  0.42nn (0.19)

 0.53/0.16  0.50/0.18  0.65/0.19

Stage 1

Total sample Children Adolescents

22 8 12

0.03 (0.09) 0.04 (0.12) 0.09 (0.15)

Stage 2

Total sample Children Adolescents

22 8 12

Stage 3

Total sample Children Adolescents

Stage 4

 0.52/0.10  0.75/0.10  0.63/  0.01

Notes: Number of studies (N), standard error (SE), 95% confidence interval (95% CI). n

po 0.05. p o0.01.

nn

pd

OL%

Q

0 0 0

Nfs 0 11 0 297 23 63

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sleep (R2 ¼0.11, po0.05), increased REM density (R2 ¼0.24, po0.01), and a greater percentage of awake time (R2 ¼0.09, po0.01). The number of previous MDD episodes and mean episode length were not predictive of any macroarchitecture feature. MDD patients with suicidal ideation had a greater percentage of awake time (R2 ¼0.05, po0.05) whereas patients exhibiting suicidal behavior had less stage 2 sleep (R2 ¼  0.02, po0.05) and increased REM density (R2 ¼0.02, po0.05). Comorbid dysthymia was predictive of increased total sleep time (R2 ¼0.04, po0.01), decreased stage 3 sleep (R2 ¼  0.02, po0.05), shorter REM latency (R2 ¼  0.02, po0.05), and increased REM sleep (R2 ¼ 0.04, po0.01). MDD patients with a comorbid anxiety disorder had more awake time (R2 ¼0.02, po0.05), shorter REM latency (R2 ¼  0.02, po0.01), and increased REM sleep (R2 ¼0.03, po0.01). Patients with comorbid ADHD had increased REM sleep (R2 ¼0.02, po0.01), and patients with other externalizing disorders spent more time awake (R2 ¼ 0.04, po0.01), had decreased sleep efficiency (R2 ¼  0.02, po0.05) and stage 3 sleep (R2 ¼  0.04, po0.05), shorter REM latency (R2 ¼  0.04, po0.01), increased REM sleep (R2 ¼0.03, po0.01) and REM density (R2 ¼0.04, po0.01). 3.2. Sleep microarchitecture The meta-analysis of four studies (Armitage et al., 2000b, 2006, 2002; Morehouse et al., 2002) reporting features of sleep microarchitecture included seven samples and a total of 213 depressed

, Cohen s d

, Cohen s d

Standard Error

, Cohen s d

Standard Error

Standard Error

, Cohen s d

, Cohen s d

Standard Error

, Cohen s d

Standard Error

Standard Error

, Cohen s d

, Cohen s d

Standard Error

, Cohen s d

Standard Error

Standard Error

, Cohen s d

Standard Error

Standard Error

Standard Error

adolescents combined. A greater percentage of REM was found in 38% of children and in 20% of depressed children and adolescents combined when compared to controls. REM latency was shorter in 22% of adolescents with MDD and in 23% of children and adolescents combined. REM density was increased in 32% of adolescents and in 37% of children and adolescents combined. Awake time was increased in 17% of depressed adolescents. The findings on total sleep time, sleep latency and percentage of REM in children, and REM density in adolescents, should be interpreted with caution since they are based on less than 10 studies (range ¼ 6–8 studies). The fail-safe N values for these findings, however, range from 11 to 36 indicating that the mean difference is robust and not likely an artifact of publication bias. There was significant variability among studies examining sleep efficiency in children and adolescents combined, percentage of REM in children and children and adolescents combined, REM latency in adolescents and children and adolescents combined, and REM density in adolescents and children and adolescents combined. Moderate publication bias was found based on funnel plot analyses for sleep efficiency, sleep latency, REM latency, REM density, and awake time (see Fig. 2). Despite this finding, the failsafe N values for these variables suggest that the results are not likely to be an artifact of publication bias. The meta-regression indicated that older MDD patients had decreased sleep efficiency (R2 ¼  0.11, po0.01), increased slow wave

15

, Cohen s d

, Cohen s d

Fig. 2. Funnel plots describing the spread of studies on features of sleep macroarchitecture. Features of sleep macroarchitecture include (A) total sleep time, (B) sleep latency, (C) sleep efficiency, (D) stage 1, (E) stage 2, (F) stage 3, (G) stage 4, (H) slow wave sleep, (I) REM sleep, (J) REM latency, (K) REM density, and (L) awake time. Asymmetry in funnel plots represents publication bias.

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Table 2 Effect sizes for features of sleep microarchitecture. Sleep feature

N

Mean d (SE)

95% CI

pd

OL%

Q

pQ

I2

Nfs

BDRCOH BDLCOH TDRCOH TDLCOH BCOH TCOH

7 7 7 7 7 7

 0.69  0.40n  0.35n  0.48n  0.43n  0.32

 1.21/  0.17  0.75/0.05  0.64/0.05  0.87/  0.10  080/0.06  0.80/0.16

0.10 0.03 0.02 0.01 0.02 0.19

58 73 75 68 70 77

34.69 16.17 11.94 19.56 18.03 30.16

o0.01 0.01 0.06 o0.01 0.01 o0.01

82.70 62.89 49.77 69.33 66.72 80.11

59 19 13 31 23 10

(0.27) (0.18) (0.15) (0.20) (0.19) (0.24)

Notes: Number of studies (N), standard error (SE), 95% confidence interval (95% CI). n

po 0.05.

patients and 163 controls. The MDD patients had a mean age of 12.957 2.03 years and a mean Tanner score of 2.9 70.83. The control group had a mean age of 12.95 72.04 years, only one study reported a Tanner score of 3.20. MDD patients had a number of comorbid diagnoses including dysthymia (24.83 717.52%), anxiety disorder (25.74 721.40%), ADHD (23.81 719.39%) and other externalizing disorders (28.08 731.99%). Two studies compared MDD patients to controls, another compared a no-recovery MDD group at follow-up to both MDD patients who had one recurrence or no recurrences, and a fourth study compared adolescent females at high risk for depression to healthy controls. Mean effect sizes for microarchitectural features are displayed in Table 2. Intrahemispheric BDLCOH was decreased in 27% of the patients with MDD when compared to a comparison group. TDRCOH and TDLCOH were also decreased in 25% and 32% of the patients with MDD, respectively. Interhemispheric BCOH was lower in 30% of patients with MDD. Significant heterogeneity of studies was found for all measures of coherence except for TDRCOH. The fail-safe N values ranging from 10 to 59 (Table 2) indicate that the coherence findings are unlikely to be artifacts of publication bias. Older patients showed poorer coherence between beta and delta in the right (R2 ¼  0.25, po0.01) and left hemispheres (R2 ¼  0.62, po0.01), between theta and delta in the right (R2 ¼  0.02, po0.01) and left hemispheres (R2 ¼ 0.22, po0.01), and for beta (R2 ¼ 0.16, po0.01) and theta (R2 ¼  0.14, po0.05) between the right and left hemispheres.

4. Discussion This meta-analysis compared sleep macro-and microarchitecture between healthy children and adolescents and those with MDD. We further examined the influence of age, MDD characteristics such as the number of previous MDEs and episode length, and comorbid psychiatric diagnoses on sleep macroarchitecture. To our knowledge, this synthetic review is among the largest to provide a comprehensive description of features of sleep macroarchitecture in early-onset depression, and to provide a preliminary examination of sleep microarchitecture in pediatric MDD. When compared to healthy children and adolescents, patients with MDD showed modest differences on sleep macroarchitecture. As previously suggested, (Dahl et al., 1996; Emslie et al., 1990) longer sleep latency remains the most robust differentiating feature of sleep macroarchitecture in early-onset depression, successfully differentiating almost a third of children and adolescents with MDD from controls. Depressed children have a longer total sleep time and more REM sleep than their healthy peers. In contrast, depressed adolescents have decreased sleep efficiency, early onset of REM sleep, increased REM density and awake time. This meta-analysis indicates that 63% to 99% of depressed youth have similar sleep macroarchitecture to controls. This overlap suggests that characteristics of sleep macroarchitecture are not effective as individual

markers for depression in children and adolescents, though they may serve as descriptive features of MDD in a subset of individuals. The heterogeneity between studies describing features of sleep macroarchitecture such as sleep efficiency, REM sleep, REM latency and REM density supports the assertion that sleep macroarchitecture differences reported in early-onset depression by individual studies are generally equivocal (Ivanenko et al., 2005; Rao, 2011). The heterogeneity within studies was most considerable for REM density, indicating that this measure may vary substantially among depressed individuals. Psychiatric disorders comorbid with MDD such as dysthymia, anxiety, ADHD and other externalizing disorders moderate sleep macroarchitecture in early-onset depression. This finding calls into question whether characteristics of sleep macroarchitecture are specific to depression, or perhaps generally represent sleep disturbance in early-onset psychopathology. For example, decreased sleep efficiency and short REM latency have been described as trends in children and adolescents with one to seven comorbid psychiatric diagnoses in an observational study of inpatients (Shahid et al., 2012). The lack of specificity of sleep features in psychiatric disorders has been previously described in adults (Benca et al., 1992). This study showed that children and adolescents with depression have decreased coherence between theta and delta in the right hemisphere, beta and delta, and theta and delta in the left hemisphere during sleep. Coherence of beta, but not theta, was decreased between the right and left hemispheres. Further, coherence between frequencies and between hemispheres became worse with age in depression. The limited number of studies available for the analysis of sleep microarchitecture and the diversity of experimental and comparison groups significantly limits the interpretation of these findings. Although individual effect sizes suggest that temporal coherence measures hold promise as markers for depression in children and adolescents, (Armitage et al., 2000a) similar extreme values are represented in the macroarchitecture literature for measures of total sleep time, slow wave sleep, REM, and REM latency (see Table 1). Other measures of temporal coherence performed at or slightly above the level of leading macroarchitectural features in differentiating patients with depression from the comparison group. Further work is needed before conclusions on the efficacy of temporal coherence measures as markers for depression can be drawn. Developmental considerations should be taken into account when evaluating sleep features of early-onset MDD. In healthy children and adolescents, total sleep time and NREM sleep concurrently decline with age, marking the most rapid period of change in sleep over the lifespan (Feinberg et al., 2012). Findings from the present study such as increased total sleep time and REM sleep in children, and decreased sleep efficiency and REM latency in adolescents, may represent a variant on the normal maturation process involving shorter sleep and decrease in NREM sleep in adolescence (Feinberg and Campbell, 2010). The age related circadian influence of chronologically later sleep times framed against an imposed schedule of school and social activities may increasingly

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exacerbate sleep disturbances after puberty and throughout adolescence (Carskadon, 2011). This meta-analysis is limited by the small number of studies on sleep macroarchitecture in children with MDD and studies on sleep microarchitecture overall. The heterogeneity in methodology between studies on factors such as inpatient versus outpatient status, imposed sleep-wake times prior to or during PSG, and prescribed medication use prior to the study period limit our findings. Further, quantitative analysis of the influence of depression severity on sleep macro- or microarchitecture was not possible due to the limited number of studies.

5. Conclusions Despite modest differences in sleep macroarchitecture between depressed children and adolescents and their healthy peers, these features of sleep are not specific biological markers for depression. The literature on sleep microarchitecture suggests that measures of temporal coherence may hold promise as markers for earlyonset depression, however, based on a limited number of studies using varied comparison groups, this meta-analysis demonstrated group differences that were modest at best. Macro- and microarchitecture features of sleep may be useful as descriptive parameters for treating MDD related sleep problems and for describing the clinical status of patients. Further investigation of sleep neurobiology in early-onset affective disorders is needed to understand the significance of these findings, which are framed by normative changes in sleep over development.

Role of funding source No financial support was provided for the present research.

Conflict of interest The authors wish to note that we have no financial or other relationships that could be interpreted as a conflict of interest affecting this manuscript.

Acknowledgments The authors thank Ms. Catharine Charlesworth for her assistance in formatting the figures for this manuscript.

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