Identifying an abnormal electroencephalographic sleep profile to characterize major depressive disorder

Identifying an Abnormal Electroencephalographic Sleep Profile to Characterize Major Depressive Disorder Michael E. Thase, David J. Kupfer, Amy J. Fasiczka, Daniel J. Buysse, Anne D. Simons, and Ellen Frank There is little agreement as to the best definition of a categorically abnormal electroencephalograph& (EEG) sleep profile to characterize major depressive disorder. Therefore, a series of classification, replication, and validation analyses were conducted to identify such a profile. The EEG sleep studies of healthy controls (n = 44), depressed inpatients (n = 44), and depressed outpatients (n = 181) were utilized, including subgroups of patients studied both before and after nonpharmacologic treatment with either cognitive behavior therapy (CBT) or interpersonal psychotherapy (IPT). A discriminant index score (based on reduced REM latency, increased REM density, and decreased sleep efficiency) was found to: 1) reliably discriminate between depressed inpatients, depressed outpatients, and controls; 2) show good test-retest reliability; and 3) identify a subset of depressed outpatients who were older, manifested a broader array of EEG sleep disturbances, and were less responsive to CBT or IPT. Posttreatment studies of patients indicated that normal sleep profiles were relatively stable, whereas abnormal profiles tended to normalize. These findings provide an empirically validated method that may improve the applicability, efficiency, and prognostic utility of EEG sleep studies of depressed patients. © 1997 Society of Biological Psychiatry Key Words: Electroencephalographic (EEG) sleep; Endogenous depression; REM sleep; Major depressive disorder; Psychotherapy response BIOL PSYCHIATRY1997;41:964--973

Introduction Major depressive disorder is a heterogeneous condition with respect to presenting symptomatology, treatment response, and clinical course. Traditionally, the degree of neurobiological disturbance accompanying depressive episodes has been suggested as a means to identify a more From the Westem Psychiatric Institute and Clinic, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA. Not for citation without written permission from the authors. Address reprint requests to Dr. Michael E. Thase, University of Pittsburgh Medical Center, Western Psychiatric Institute and Clinic, 3811 O'Hara Street, Pittsburgh, PA 15213. Received June 13, 1995; revised January 30, 1996.

© 1997 Society of Biological Psychiatry

autonomous subgroup that requires pharmacotherapy or electroconvulsive therapy (Free and Oei, 1989; Thase and Kupfer 1987). All-night electroencephalographic (EEG) sleep recordings have been studied extensively in depressed patients as one such measure (e.g., Benca et al 1992; Buysse and Kupfer 1993). Disturbances of EEG sleep, such as reduced rapid eye movement (REM) latency, poor sleep efficiency, and increased phasic REM activity, are hypothesized to reflect a more basic neurophysiologic substrate of severe episodes of depression (Buysse and Kupfer 1993); however, the utility of pretreatment EEG sleep studies for prediction of response to various treatments of depression is not well established. 0006-3223/96/$17.00 PII S0006-3223(96)00259-4

EEG Sleep Profile in Major Depression

For example, although some studies suggest that patients with reduced REM latency may respond preferentially to antidepressant pharmacotherapy (e.g., Akiskal et al 1980; Rush et al 1985, 1989; Svendson and Christensen 1981) or more poorly to placebo (Coble et al 1979; Heiligenstein et al 1994; Zamitt et al 1988), a number of more recent investigations have failed to find the predicted relationship between reduced REM latency and poorer response to psychotherapy (Buysse et al 1992a; Corbishley et al 1990; Jarrett et al 1990; Simons and Thase 1992; Thase et al 1993). A major problem complicating this line of research is the lack of consensus about the criteria used to define an "abnormal" sleep profile (Benca et al 1992; Buysse and Kupfer 1990). Studies of the diagnostic performance of selected EEG sleep parameters document that individual sleep variables, such as reduced REM latency, have limited specificity, with false-positive rates of 20%, 40%, or even higher (e.g., Benca et al 1992; Mendlewicz and Kerkhofs 1991; Thase and Kupfer 1987). Moreover, even though reduced REM latency has been widely replicated in studies of depressed patients, recent research has documented this abnormality in several high-risk but nondepressed groups, including personality disorder patients (Battaglia et al 1993), remitted mood disorder patients (Buysse et al 1992b; Giles et al 1993; Rush et al 1986; Steiger et al 1989; Thase and Simons 1992), and first-degree relatives of affected probands (Giles et al 1989; Krieg et al 1990). Thus, variables such as REM latency may be more closely related to vulnerability to depression than to the pathophysiology of acute depressive states (Kupfer and Ehlers 1989). Previously, several groups addressed the problem of classifying the EEG sleep profiles of depressed patients by using multivariate statistical techniques to construct composite scores based on two or more variables (Feinberg and Carroll 1984; Gillin et al 1979; Kupfer et al 1978; Thase et al 1986). Such composites have been proposed to yield more reliable or robust classifications of neurobiologically disturbed cases (Buysse and Kupfer 1990); however, this approach has not yet been widely adopted and external validation of specific classifications has been largely lacking. The multivariate classifications developed to date also may have limited applicability to outpatient samples, in which patients typically have less disturbed EEG sleep profiles (e.g., Buysse et al 1991; Dahl et al 1990; Reynolds et al 1982; Thase et al 1993). Over the past 4 years, we have studied a composite EEG sleep measure, based on sleep efficiency, REM latency, and REM density, that shows particular promise for characterizing major depressive episodes (Thase et al 1993, 1996). In the current report, the development,

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replication, and validation of this EEG sleep profile is described.

Method Patient and Controls Subjects for this study included 44 inpatients and 181 outpatients meeting DSM-III-R criteria for major depressive disorder who were enrolled in one of three research studies conducted at the Western Psychiatric Institute and Clinic of the University of Pittsburgh School of Medicine between 1987 and 1993 (Buysse et al 1992a; Thase et al 1991, 1996). All patients also met research diagnostic criteria (RDC; Spitzer et al 1978) for the diagnosis of primary major depressive disorder on the basis of the Schedule for Affective Disorders and Schizophrenia interview (SADS; Endicott and Spitzer 1978). Illness severity was assessed with the 17-item Hamilton Depression Rating Scale (HDRS; Hamilton 1960), the Global Assessment Scale (GAS; Endicott et al 1976), and the Beck Depression Inventory (BDI; Beck et al 1961). All patients had HDRS scores of -> 14 at intake. Exclusion criteria included bipolar I disorder, psychotic subtype of major depression, and serious Axis I or Axis II comorbidities including drug or alcohol abuse within 6 months of intake. In addition, chronicity (i.e., either a depressive episode of > 2 years' duration or an antecedent history of dysthymic disorder) led to exclusion from research participation. Patients were also screened with a comprehensive physical examination and laboratory studies to exclude those with illnesses or taking medications known to either cause depression or invalidate EEG sleep studies. All patients provided written informed consent for research participation. Forty-four healthy controls were selected from our Mental Health Clinical Research Center database to serve as a normal comparison group. Controls were interviewed with the lifetime version of the SADS (SADS-L) (Spitzer and Endicott 1975) to rule out any potential subjects with a personal history of psychopathology, and a physical examination and battery of laboratory studies were completed to ensure that they were healthy. All controls scored < 5 on the HDRS. Controls were studied in the same years as the inpatients, using the same assessment methodologies, and were selected to equate the normal comparison group with the inpatient groups with respect to age, age range, and proportion of men and women. The control group did not differ significantly from the inpatient group in these respects (see Table 1). Controls were not prescreened to exclude those with family histories of affective disorder or other psychopathology. Similarly, family history data were not systematically collected for the patient groups.

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Table 1. Comparison of Pretreatment Sleep Variables (Means and SD) in Healthy Controls (C), Depressed Inpatients (IP), and Depressed Outpatients (OP)

Variable Age (years) Age range Sex (M:F) Time spent asleep (min) Sleep latency (min) a A w a k e (min) e Awakenings (#) Awake last 2 hours (min) e Sleep efficiency (%)a Stage 1 (%)e Stage 2 (%) Slow Wave Sleep (%)e R E M (%) R E M (min) R E M activity (units) e R E M density (units/min) REM latency (min) REM periods (#) R E M intensity (units/min)

Healthy controls (n = 44)

Depressed inpatients (n = 44)

Depressed outpatients (n = 181)

A N C O V A (df = 2,265) Age F

Group F

31.5 (10.4) 17-56 16/28 415.2 (44.8) 17.5 (13.9) 17.4 (22.9) 4.6 (2.3) 6.8 (9.7) 92.3 (6.1) 4.2 (2.3) 58.6 (8.2) 13.9 (6.9) 23.1 (5.3) 97.3 (27.4) 139.6 (62.7) 1.4 (0.4) 83.5 (26.2) 3.9 (0.9) 0.3 (0.1)

33.1 (10.2) 19-64 19/25 374.7 (51.2) 28.6 (23.3) 27.7 (30.9) 4.8 (3.0) 14.8 (21.4) 86.9 (8.1) 4.2 (2.4) 57.3 (7.6) 10.8 (7.6) 27.6 (6.9) 104.2 (28.3) 172.8 (77.6) 1.6 (0.5) 55.9 (24.1) 4.0 (0.8) 0.5 (0.2)

36.9 (9.3) 20-69 66/115 394.9 (49.5) 23.4 (16.3) 30.2 (27.1) 5.8 (2.9) 14.8 (14.4) 88.2 (7.2) 4.9 (2.8) 60.1 (7.8) 11.1 (8.0) 23.9 (5.0) 95.0 (24.3) 146.4 (67.4) 1.5 (0.5) 71.5 (23,2) 3.8 (0.8) 0.4 (0.2)

--__ 19.95 c 0.48 52.43 ~ 17.73 c 45.46 c 31.15 C 13.43 C 36.04 c 64.17 c 0.07 4.50 ~ 1.61 0.07 2.45 11.65 e 0.11

6.89 b -×2 = 0.71 7.68" 6.01 b 2.77 2.10 4.16 ¢ 9.09 " 0.51 0.85 1.26 9.81' 1.74 3.06 r 2.63 14.76 " 0.15 7.80 "

Tukey's ~

C C C C C

C

C C

C < OP --> OP > IP < OP = IP < OP = IP ns < IP = OP > OP = IP ns ns ns = OP < IP ns ns ns < OP < IP ns = OP < IP

aTukey's Honestly Significant Difference test used for post hoc between-group comparison. bp < .01. ~p < .001. aLog transformed values used for statistical test. eSquare root transformed values used for statistical test.

se < .05.

EEG Sleep Studies Following a 14-day medication-free washout (including at least 7 days of hospitalization for inpatients), all patients and controls underwent at least 2 consecutive nights of initial (T1) EEG sleep assessment. During this interval, subjects were instructed to avoid napping and to keep a regular sleep-wake schedule (i.e., shift work was not permitted). Sleep studies included a routine polysomnographic montage consisting of one channel of EEG recording (C3 or C4, referenced to A1-A2), bilateral electro-oculograms (EOG; references to A1-A2), and bipolar submental electromyograms (EMG). High- and low-frequency filter settings were 30 and 0.3 Hz, respectively, for EEG and EOG, and 90 and 10 Hz for EMG. Paper speed was 10 mm/sec, and sensitivity on the EEG channel was 7.5 ixV/mm. EEG sleep records were scored in 60-sec epochs using standard criteria (Rechtschaffen and Kales 1968). Sleep onset was defined as the first of 10 consecutive minutes of stage 2 or deeper non-REM sleep, interrupted by no more than 2 rain of stage 1 or wakefulness. REM latency was defined as the interval between sleep onset and the first period of at least 3 consecutive minutes of REM sleep, minus minutes of intervening awakening (Reynolds et al 1983). Records were scored by certified polysomnographic technologists without knowledge of

diagnostic group or phase of treatment. All variables used in this report were scored with interrater reliability coefficients of > .85. Throughout the 6-year study period, technologists attended monthly training sessions to ensure continued high levels of reliability. Automated period analyses of delta waves and REM counts were not available for the first wave of inpatients and, accordingly, are not used in this report. The visual scoring method used by our laboratory yields more than 30 different measures of sleep maintenance, sleep architecture, and the phasic and tonic aspects of REM sleep; however, this variable set can be reduced to 16 representative measures (see Table 1) by eliminating highly redundant variables. For example, sleep efficiency was used instead of the highly correlated measure of sleep maintenance. Similarly, minutes and percentages of time spent in stages 3 and 4 of slow wave sleep were excluded in favor of a single measure, percentage of slow wave sleep. Results are reported as the means of the first 2 nights of study to lessen the impact of night-to-night variations on sleep parameters. Based on prior studies, sleep variables typically showing skewed or otherwise nonnormal distributions were routinely transformed using either log or square root transformations prior to their use in statistical analyses.

EEG Sleep Profile in Major Depression

Treatment Patients initially received nonpharmacologic treatment to facilitate longitudinal study of EEG sleep profiles by avoiding the potential problems associated with pharmacotherapy (e.g., drug withdrawal rebound effects or rapid relapse following medication discontinuation) (see Thase and Simons 1992). Depending on the specific protocol, patients received one of three types of psychotherapy. In study 1, inpatients (n = 44) received an intensified course of cognitive behavior therapy (CBT), delivered using a treatment manual that featured daily individual sessions (Thase and Wright 1991). In outpatient study 2 (n = 90), patients were treated with the conventional 20-session, 16-week CBT protocol (Beck et al 1979). The remaining 91 outpatients in study 3 were treated with weekly sessions of interpersonal psychotherapy (IPT; Klerman et al 1984). The latter group differed from those treated with CBT in two major ways: 1) the IPT protocol was administered more flexibly, with the course of therapy typically ranging from 8 to 20 sessions (mean: 12.6 sessions; SD: 7.6 sessions); and 2) the study utilizing IPT was restricted to patients with recurrent depressive episodes, whereas the other two studies included both first-episode and recurrent cases.

Follow-Up EEG Sleep Assessments A second set of EEG sleep studies was available for 142 patients after nonpharmacologic treatment. Follow-up (Y2) sleep studies were conducted using the same procedures as pretreatment (T~) studies. Repeat (T2) sleep studies were obtained for 22 inpatients [mean duration between studies: 16.6 (3.7) days], 78 CBT-treated outpatients [mean duration between studies: 22.9 (7.2) weeks], and 42 IPTtreated outpatients [mean duration between studies: 26.8 (9.1) weeks]. For the purposes of this report, a remission was defined as consecutive HDRS scores of < 8 at the time of T z studies (i.e., for at least 4 weeks). By design, none of the inpatients were fully remitted at the time of T 2 studies.

Data Analysis Plan Our initial aim was to identify a sleep profile that would reliably distinguish between depressed patients and healthy controls. We reasoned that because inpatients have more severe EEG sleep disturbances than outpatients (see Table 1), they would be the more' informative pathologic criterion group. Depressed inpatients were grouped sequentially on the basis of time of entry into the study. The first wave (n = 20), which entered the study between 1989

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and 1991, was used to derive the initial discriminant classification. This group included 8 men and 12 women (mean age: 35.2 years; standard deviation: 10.2 years). They were moderately-to-severely depressed (mean HDRS after washout: 21.9; SD: 3.9), and the group included both single-episode (n = 10) and recurrent (n = 10) subtypes. The second wave (n = 24), entering between 1991 and 1993, was used to replicate the findings derived from the first wave. The second wave of inpatients was comparable to the first: 11 men and 13 women; mean age = 31.7 years (SD: 9.8 years); postwashout HDRS = 21.5 (SD: 4.5): There were 12 single-episode cases and 12 recurrent cases in the second wave. The first step in the analytic plan was to identify a set of EEG sleep variables that were significantly more disturbed in the first wave of depressed inpatients (n = 20) relative to healthy age-matched controls. There were 5 men and 15 women in the control group, i.e., a proportion that did not differ significantly from the inpatient group (X2 = 1.0, df = 1, p = .31). A series of t tests was performed and the significant variables were entered into a step-wise discriminant analysis to identify the subset of variables that most accurately classified cases as depressed versus normal.

Second, the classification was replicated in the second set of inpatients and age- and sex-matched controls. The accuracy of the replication was tested with a X2 test (two tailed) of the resultant 2 × 2 contingency table. Third, although a strict test of test-retest reliability was not possible, reliability was estimated using the data from the 22 unmedicated inpatients who underwent two sets of sleep studies. This group was considered suitable to approximate test-retest reliability because their sleep studies were only 2-3 weeks apart and, by definition, none of the patients had achieved remission at T 2. Reliability was quantified using a Pearson correlation coefficient (for discriminant index scores, a continuous measure) and the kappa (K) statistic (for categorical agreement) (Cohen, 1960. Fourth, the two outpatient series were classified using the discriminant index method. The proportions of normal and abnormal cases were compared, both within and across these series, with X2 tests (two tailed). In the fifth step, the outpatients were subdivided into normal (n = 100) and abnormal (n = 81) sleep groups on the basis of their T 1 profiles. Demographic, clinical, and EEG sleep characteristics of these groups were next compared, using either two-tailed t tests, ×2 tests, or analyses of covariance (ANCOVAs) (covariate: age), as appropriate. We predicted that, if valid, the abnormal sleep classification would identify patients with more severe symptomatology (both before and after treatment) and a

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Table 2. Pretreatment EEG Sleep Variables in the Initial Cohorts of Depressed Inpatients and Age-Matched Healthy Controls Healthy controls (n = 20) Sleep continuity variables Time spent asleep (min) Sleep latency (min)b Awake (min) Number of awakenings Awake last 2 hours (min) Sleep efficiency (%) NREM sleep variables Stage 1 (%)d Stage 2 (%) Slow wave sleep (%)d REM variables REM (%) REM (min) d REM activity (units)d REM density (units/min) REM latency (min)a REM periods (#) REM intensity (units/min)

422.5 (39.0) 15.1 (11.9) 11.9 (9.1) 4.3 (2.4) 4.4 (4.4) 94.0 (3.0) 4.1 (2.2) 58.1 (7.7) 13.0 (6.7)

Depressed inpatients (n = 20) 353.2 34.6 29.7 3.5 19.4 84.6

(38.2) (28.9) (35.3) (2.0) (27.7) (8.2)

3.6 (2.5) 59.6 (9.0) 10.9 (7.0)

24.8 (3.4) 25.9 (6.8) 105.2 (18.9) 92.8 (27.3) 151.5 (50.8) 143.4 (59.1) 1.40 (0.27) 1.50 (0.49) 74.9 (19.7) 53.2 (21.0) 4.1 (0.8) 3.7 (0.7) 0.36 (0.11) 0.40 (0.15)

t (df = 38) 5.75" -2.71" -2.18 c 1.07 -2.31 c 4.85 a 0.89 -0.57 0.77 -0.65 1.87 0.73 -0.78 3.42" 1.61 -0.97

"p < .01. bLog transformed values used for statistical test. Cp < .05. dSquare root transformed values for statistical test.

greater degree of disturbance on the sleep measures not included in the discriminant classification. The sixth step examined the longitudinal stability of the abnormal and normal sleep classifications. We predicted that normal T l profiles would be highly stable at T 2, whereas abnormal EEG sleep profiles would be at least partly state-dependent or reversible upon remission of the depressive episode. At T 2, 85 (71%) of the 120 unmedicated outpatients restudied were in remission. Stability was quantified using correlation and kappa statistics.

Results

Step 1. Identifying the Profile The initial groups of 20 depressed inpatients and agematched controls differed at the p < .05 level on 6 of 16 comparisons. The variables differing between these groups included: time spent asleep (TSA), sleep latency (SL), minutes awake (A), minutes awake in the last 2 hours of sleep (AL2), sleep efficiency (SE), and REM latency (RL) (see Table 2). One widely replicated EEG sleep correlate of severe depression, REM density (RD), did not differ significantly between groups, although the depressed group had significantly greater variability on this measure.

As this was due to the existence of a subgroup of patients with very high values, REM density also was included. A correlation matrix based on these seven "candidate" variables was constructed to examine the relationships between the measures. As might be expected, the five variables measuring aspects of sleep continuity (TSA, SL, A, AL2, and SE) were significantly intercorrelated, with the correlations between SE, TSA, and A exceeding .7. Consequently, TSA and A were dropped from the analysis in favor of SE, which is partly derived from TSA and A. The remaining five variables were entered into a stepwise discriminant analysis intended to identify group membership. A critical p value of .15 was chosen to remove extraneous variables from the model. The final model included three variables: SE (standardized discriminant coefficient = -.98), RL (standardized discriminant coefficient = -.62), and RD (standardized coefficient = .34). A discriminant index score [-20.5 + (.0519 × RL) - (1.61 X RD) + (0.22 × SE)] based on this model was computed using untransformed values for each subject by subtracting a score derived from the controls from one derived from the depressed inpatients. Scores of zero or lower indicated abnormality. Fifteen depressed inpatients (75%) and 19 (95%) controls were correctly classified using this method (X2 = 20.4, df = 1, p < .0001). Because of the marginal contribution of RD, the model was recalculated using only two variables, SE and RL. The two variable discriminant function identified 19 (95%) controis but only 12 (60%) inpatients. Therefore, the three variable model was chosen for use in the subsequent analyses.

Step 2. Inpatient Replication The discriminant classification was next applied to the replication series, consisting of the second wave of depressed inpatients (n = 24) and age- and sex-matched controls (n = 24). A total of 20 (83%) inpatients and 20 (83%) of the controls were correctly classified (X2 = 21.3, df = 1, p < .0001). The overall "hit rate" in the replication series was almost identical to that observed in the original series (i.e., 83% vs. 85%).

Step 3. Estimating Test-Retest Reliability Among the 22 inpatients restudied, the test-retest reliability of the discriminant index score was estimated as r = .68 (p < .01). Nineteen patients (86%) had the same classification at both T 1 and T 2 (K = .71, standard error = 0.21, p = .001), including 7 (88%) of the 8 patients with normal T 1 studies and 12 (86%) of the 14 patients with abnormal T l studies. These tests probably underestimate

EEG Sleep Profile in Major Depression

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100 90 =E n,.

(N=44)

80

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z lid

70 60

14.1

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50

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I-z w ¢~

30 20

a.

10

pressed inpatients) according to the discriminant index. This included 40 (44%) of the 90 patients in study 2 and 41 (45%) of the 91 patients in study 3. The remaining 100 patients (55%) were considered to have normal sleep profiles (i.e., similar to healthy controls). As illustrated in Figure 1, both outpatient series had a significantly higher proportion of abnormal cases than the healthy controls, but significantly lower rates of test abnormality than the depressed inpatients.

Step 5. Validating the Abnormal Profile

0

CONTROLS

OUTPATIENT

OUTPATIENT

STUDY 2

STUDY 3

INPATIENTS

Figure 1. Comparison of the proportion of abnormal sleep studies in healthy controls, depressed outpatients, and depressed inpatients. The control group has a significantly lower proportion of abnormal profiles than either the outpatients (×2 = 40.5, df = 1, p < .0001) or the inpatients (×2 = 41.3, df = 1, p < .0001). The inpatients also have a significantly higher proportion of abnormal cases than the outpatients (×2 = 17.2, df = 1, p < .001). The proportions of abnormality observed in the two outpatient groups were virtually identical (×2 = 0.01, df = 1, p = .934). the inherent reliability of the classification; the average HDRS score of the study group at T 2 was only 10.1 (5.5), indicating that patients were already partially remitted.

Step 4. Outpatient Classification In the outpatient series, 81 (45%) patients were classified as having abnormal sleep profiles (i.e., similar to de-

The outpatients with abnormal and normal sleep profiles were next compared on demographic, clinical, and treatment outcome variables (see Tables 3 and 4). Patients with abnormal sleep profiles were, on average, 5 years older than patients with normal profiles, but otherwise the groups were similar with respect to demographic and pretreatment clinical variables; however, patients with abnormal pretreatment sleep studies had significantly higher HDRS and GAS ratings at posttreatment. A smaller difference was observed on the posttreatment BDI, and this was not significant (p = .088). These findings were unchanged when the between-groups age difference was controlled using ANCOVAs. The abnormal sleep group also had a significantly lower remission rate than the normal sleep group (41% vs. 62%; see Table 3). EEG sleep measures were next compared between the abnormal and normal sleep groups. A N C O V A s (covariate: age) were used for these comparisons because of the age difference between the groups. Excluding the three criterion variables (sleep efficiency, REM latency, and REM density), the groups differed at the p -< .004 level (the

T a b l e 3. D e m o g r a p h i c a n d C l i n i c a l V a r i a b l e s in O u t p a t i e n t s w i t h N o r m a l o r A b n o r m a l P r e t r e a t m e n t S l e e p P r o f i l e s Abnormal (n = 81)

Normal (n = 100)

[mean (SD)]

[mean (SD)]

Significance

Demographics A g e (years) Sex (M/F) A g e of onset of first episode (years) Recurrent (yes/no) Pretreatment ratings

39.8 (9.8) 33/48 29.6 (11.0) 63/18 (77%)

34.5 (8.2) 33/67 26.7 (9.0) 71/28 (71.8%)

HDRS ~ GAS ~ BDI" Posttreatment ratings d

20.1 (4.7) 54.5 (9.1) 25.7 (8.1)

19.7 (3.9) 55.5 (7.1) 25.0 (7.9)

HDRS GAS BDI Remission rate

8.6 74.5 11.1 41%

6.5 79.5 8.7 62%

~For GAS, abnormal n = 78, normal n = 98. bDegrees of freedom adjusted for heterogeneous variance. CFor BDI, abnormal n = 64, normal n = 75. '~For these analyses, abnormal n = 80.

(6.5) (13.7) (9.5) (33/81)

(5.8) (13.2) (8.7) (62/100)

t = ×2= t = ×2 =

3.98 1.16 1.97 0.86

t = 0.62 t = -0.79 t = 0.49 t t t ×2

= = = =

2.00 -2.47 1.72 8.11

df df df df

= = = =

179 1 179 1

df = 179 df = 142.7 b df = 162 df df df df

= = = =

178 178 178 1

< .001 = .28 = .05 = .35 = .54 = .43 = .63 = .047 = .014 = .088

=.004

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Table 4. Comparison of EEG Sleep Variables in Patients Classified by Normal or Abnormal Pretreatment Sleep Profiles Abnormal (n = 81) Sleep continuity Time spent asleep (min) Sleep latency (min)b Awake (min)c Awakenings (#) Awake last 2 hours (min)c Sleep efficiency (%)b,d NREM sleep Stage 1 (%)c Stage 2 (%) Slow wave sleep (%)c REM measures REM (%) REM (rain) REM activity (units)c REM density (units/min)d REM latency (min)c'd REM periods (#) REM intensity (units/min)

ANCOVA (F1,178)

Normal (n = 100)

Age

Group

410.6 (45.0) 18.4 (12.0) 19.5 (16.4) 5.3 (3.0) 9.2 (9.2) 91.7 (4.7)

8.654 2.28 21.00 ~ 14.39" 9.80 a 10.36 a

16.78" 28.7(P 34.06" 2.27 31.37 a 69.72 °

5.4 (2.7) 60.1 (7.7) 10.2 (7.3)

4.4 (2.9) 60.1 (7.9) 11.7 (8.4)

10.00a 16.68 ~ 26.18 a

3.74 1.36 0.07

24.2 (5.0) 92.1 (24.4) 158.5 (77.9) 1.7 (0.5) 64.5 (20.6) 3.7 (0.7) 0.4 (0.2)

23.6 (5.0) 97.3 (24.1) 136.6 (56.0) 1.4 (0.4) 77.1 (23.7) 4.0 (4.7) 0.3 (0.1)

0.44 3.61 2.04 0.01 0.40 3.64 0.55

1.09 0.68 6,10 20,04' 13,17 a 3,31 15,77 a

375.5 29.6 43.4 6.4 21.6 83.9

(48.2) (18.6) (31.6) (2.7) (16.6) (7.4)

ap < .004 (Bonferroni-adjusted significance level). ~x~g transformation used for statistical test. CSquare root transformation used for statistical test. aAs this variable was used in defining group membership,it cannot be considered a validator.

Bonferroni-corrected significance level) on 5 of the 13 comparisons (see Table 4).

Step 6. Assessing Stability Among the 120 outpatients restudied, a T~ to T 2 correlation coefficient of r = .43 (p = .001) was observed. A K of .28 (standard error = 0.09, p < .0001) similarly documented a relatively low level of categorical stability. Specifically, only 67% (80/120) of patients had the same classifications at T t and T2; however, there was 81% stability in the 72 patients with normal T 1 profiles and only 46% in the 48 patients with abnormal T 1 profiles (×2 = 15.62, df = 1,p < .0001). Thus, 54% of the abnormal T~ profiles had "normalized" following nonpharmacologic treatment.

Discussion We found that a composite measure of EEG sleep disturbance based on three variables, REM latency, REM density, and sleep efficiency, reliably discriminated between depressed patients and healthy controls. The construct validity of this classification is supported by the fact that the three constituent variables have been consistently associated with more severe endogenous or melancholic states (Benca et al 1992; Feinberg and Carroll 1984; Gillin et al 1979; Kupfer et al 1978; Thase et al 1986). The replicability of the classification across both inpatient and

outpatient series, its test-retest reliability, and its association with a broader array of EEG sleep disturbances further bolster confidence in its validity. The discriminant index studied in the current report shares certain commonalities with those published previously (Feinberg and Carroll 1984; Gillin et al 1979; Kupfer et al 1978; Thase et al 1986). Like those identified by Feinberg and Carroll (1984) and Kupfer et al (1978), the index includes REM latency and REM density, albeit at less pathologic criterion levels. Like the functions reported by Gillin et al (1979) and Thase et al (1986), a measure of sleep continuity disturbance figured prominently in the classification. Unlike its predecessors, however, the classification studied in the current report has been tested in a large group of outpatients, i.e., the most prevalent group of mood disorder patients and the group for whom empirical guidance for selection of treatment type is most sorely needed. It is important to note that a majority (55%) of the outpatients studied had "normal" sleep profiles. This is consistent with several earlier studies from our laboratory (Buysse et al 1990; Dahl et al 1990; Thase et al 1993), as well as the literature examining hypothalamic-pituitaryadrenocortical function in depression (e.g., Carroll et al 1981). Contrary to our prediction, outpatients with abnormal sleep profiles also were not significantly more symptomatic prior to treatment than patients with more normal

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profiles. Despite these findings, however, the abnormal EEG sleep profile was associated with a poorer response to treatment with either IPT or CBT. In this regard, hypercortisolism also has been associated with poor response to both placebo (see Ribeiro et al 1993) and several forms of psychotherapy (Corbishley et al 1990; McKnight et al 1992; Robbins et al 1989; Thase et al 1993). This type of predictive validity further links the abnormal sleep profile to an endogenous or biological subtype of major depressive disorder that may warrant somatic treatment (e.g., Feinberg and Carroll 1984; Rush and Weissenburger 1994; Thase 1994; Zimmerman and Spitzer 1989). The relatively low prevalence of abnormal sleep profiles in our depressed outpatients may help to explain why investigators have not found strong associations between single EEG sleep variables and response to psychotherapy (Buysse et al 1992a; Corbishley et al 1990; Jarrett et al 1990; Simons and Thase 1992). Specifically, if only about 45% of depressed outpatients have the type of neurobiological profile that is associated with poorer responses to psychotherapy, then research samples of at least 80 subjects would be necessary to have the statistical power to reliably detect the impact of such a subgroup. Several limitations of our study warrant discussion. First, while identification of an objective, empirically based neurobiologic classification of major depression has merit (e.g., Feinberg and Carroll 1984; Kupfer and Thase 1989), the expense and inconvenience of EEG sleep assessments limit their utility for routine clinical practice. Second, our results may not be fully generalizable to the total population of people with major depressive disorder. This is partly because of the sample's relatively homogenous nature (i.e., chronic depressions and patients with serious comorbidities were excluded from the study). We also did not study older patients, which enabled us to sidestep the potential confounds resulting from age-dependent changes in EEG sleep (Gillin et al 1981; Lauer et al 1991; Ulrich et al 1980). Although covarying age did not eliminate any of the key findings of the current report, it is likely that further adjustments in the classification will be necessary for use in studies of older samples. We will address this problem using Receiver Operating Characteristics (ROC) analyses (Kraemer 1992) in a future report. Conversely, other adjustments may also be necessary in studies of even younger patient groups, particularly those with hypersomnolence (Thase et al 1989) or less marked sleep-onset difficulties (Feinberg et al 1982). Third, it is likely that higher rates of "false-positive" abnormality would have been observed if patient control groups had been studied, particularly patients with alcoholism (Gillin et al 1990), borderline personality disorder

(Battaglia et al 1993), obsessive-compulsive disorder (Insel et al 1982), mania (Hudson et al 1992), or schizophrenia (e.g., Zarcone et al 1987). Of course, patients with these disorders often manifest significant depressive symptomatology. It is also plausible that the psychopathologic significance of this abnormal EEG sleep profile may extend across diagnostic classifications. For example, we note that each of the conditions listed above are notoriously hard to treat with psychotherapy alone. Fourth, the "behavior" of the abnormal profile has not been fully established as reversible or state-dependent; nearly half of the patients with abnormal T a profiles continued to show this disturbance at posttreatment. Further study of the reversibility of apparently persistently abnormal profiles is needed, as are studies of its relationship to vulnerability to recurrent depression. In this regard, we recently found that CBT-treated patients with abnormal T 1 sleep profiles were at significantly increased risk of recurrent depression during a 3-year longitudinal follow-up (Thase et al 1996). Finally, our study did not capitalize on several more recent technological and methodological advances in sleep research. For example, it is possible that use of more finely grained, computer-scored measures of REM and slow wave sleep may further enhance the accuracy of the discriminant classification. Nevertheless, the visually scored measures can be more easily obtained by a greater number of sleep laboratories, resulting in a broader applicability. Also, this study was initiated before a number of factors known to potentially affect EEG sleep, such as season of year, menstrual status, body mass index, diet, and deviations from habitual sleep onset and "good morning" times, were routinely controlled in the experimental methods. Implementation of a more rigorous protocol dealing with these factors could improve the accuracy of the classification. Despite these limitations, this multivariate classification method shows promise for improving the applicability, efficiency, and prognostic value of EEG assessments of patients with depressive disorders. Replication by other groups and extension of these findings to a broader range of patients and treatments are now needed.

This work was supported in part by grants MH-41884 and MH-30915 (MHCRC) from the National Institute of Mental Health, as well as a grant from the John D. and Catherine T. MacArthur Foundation's Depression Network. We wish to thank Mr. Tim Harden, Ms. Andrea Emling, Ms. Leslie Vasey. Ms. ChristineJohnson,and Ms. Lisa Stuparfor their assistancein preparation of this manuscript.

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