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Original Article |

Electroencephalographic Sleep Profiles Before and After Cognitive Behavior Therapy of Depression FREE

Michael E. Thase, MD; Amy L. Fasiczka, MA; Susan R. Berman; Anne D. Simons, PhD; Charles F. Reynolds III, MD
[+] Author Affiliations

From the Western Psychiatric Institute and Clinic, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pa (Drs Thase and Reynolds and Mss Fasiczka and Berman); and the Department of Psychology, University of Oregon, Eugene (Dr Simons).


Arch Gen Psychiatry. 1998;55(2):138-144. doi:10.1001/archpsyc.55.2.138.
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Background  Previous studies have not fully resolved the state-dependent vs traitlike behavior of the electroencephalographic sleep abnormalities associated with depression. We therefore examined the sleep profiles of depressed patients before and after 16 weeks of treatment with cognitive behavior therapy to determine the stability or reversibility of selected abnormalities.

Methods  Seventy-eight unmedicated patients with major depressive disorder were stratified into abnormal and normal subgroups on the basis of pretreatment sleep study results. Two prospectively defined types of sleep variables were studied: those expected to be traitlike or state independent (type 1) and those predicted to be reversible or state dependent (type 2).

Results  The type 1 sleep disturbances (reduced rapid eye movement latency, decreased delta sleep ratio, and decreased slow wave sleep [in percentage]) were stable, as predicted, across time. A composite measure of type 2 disturbances (based on rapid eye movement latency, sleep efficiency, and rapid eye movement density) improved significantly, although a minority of patients in remission had persistent abnormalities.

Conclusions  The electroencephalographic sleep correlates of depression can be disaggregated into state-independent and partially reversible subgroups. Persistent sleep disturbances in remitted patients may have ominous prognostic implications.

LONGITUDINAL studies of electroencephalographic (EEG) sleep profiles in patients with depression suggest that some disturbances, such as increased rapid eye movement (REM) density and poor sleep efficiency, are reversible or state-dependent abnormalities.13 Yet other features, notably decreased slow wave sleep, seem to be more persistent or traitlike.13 One of the most widely replicated EEG sleep abnormalities, reduced REM latency, may exhibit both state-dependent and traitlike behaviors.4 It is important to differentiate between state-dependent and traitlike abnormalities for several reasons.24 State-dependent disturbances are more likely to reflect neurobiological processes underlying an acute episode of illness and, thus, may help identify target mechanisms for treatment. For example, the association of hypercortisolism, increased phasic REM sleep, and sleep continuity disturbances may identify a state of central nervous system hyperarousal that is more responsive to somatic than psychosocial therapies.57 Conversely, state-independent "markers" are useful to study familial transmission of disorders and to identify patients at increased risk for relapse or recurrence.3,4 The traitlike nature of reduced REM latency and decreased slow wave sleep is suggested by research using family studies,811 longitudinal follow-up,1214 and other high-risk paradigms.15,16

Most previous follow-up studies of EEG sleep have focused on patients treated with pharmacotherapy.12,13,1723 Pharmacotherapy may introduce several potential confounds, however, including the distortion of sleep profiles by extraneous medication effects (eg, antihistaminic effects), "rebound" sleep disturbances triggered by drug discontinuation, and loss of informative cases due to rapid relapse after withdrawal of medication.2426 Nonpharmacological treatments such as interpersonal psychotherapy27 or cognitive behavior therapy (CBT)28 might obviate these confounds. However, this strategy has other potential shortcomings. Specifically, sample composition may be biased by the differential "sieve" effect, in which neurophysiologically disturbed patients may be less responsive to psychotherapy57 or unavailable because of recurrence during longitudinal follow-up.5,6 Such research is also hampered by the low prevalence of EEG sleep abnormalities typically seen in depressed outpatients.5,6,14,21,2426 Simply put, sleep profiles that are undisturbed at pretreatment will not "normalize" further after recovery.

In this study, the sleep profiles of 78 depressed outpatients are examined before and after treatment with CBT. Changes in sleep profiles are studied with respect to the level of pretreatment abnormality and the predicted behavior of selected sleep disturbances (ie, traitlike vs reversible). We hypothesize that the sleep disturbances proposed to be traitlike will persist into remission, whereas the presumed state-dependent disturbances will normalize after effective nonpharmacological treatment.

PATIENTS

The procedures used for recruitment, assessment, and treatment have been described in detail in earlier reports.5,29 To summarize, outpatients meeting DSM-III-R30 criteria for nonbipolar, nonpsychotic major depressive disorder were referred to our research team for a detailed secondary evaluation that included an independent research interview using both the Schedule for Affective Disorders and Schizophrenia31 and the Hamilton Rating Scale for Depression (HRSD).32 Patients were eligible for the study if they (1) met Research Diagnostic Criteria33 for a diagnosis of primary (nonpsychotic) major depression, probable or definite endogenous subtype; (2) scored more than 14 on the 17-item HRSD; and (3) did not have untreated or poorly controlled general medical conditions that might cause depression (eg, hypothyroidism), confound EEG sleep studies (eg, sleep apnea), or require treatment with psychoactive agents (eg, corticosteroids or β-blockers). Exclusion criteria included DSM-III-R dysthymia, chronic (>18 months' duration) major depression, a history of substance abuse within the past 2 years, and well-established Axis II diagnoses of borderline or antisocial personality disorders. These criteria were chosen to maximize internal validity by reducing other known sources of outcome variance.5

Among the 90 outpatients who entered our treatment study,5 78 (87%) completed posttreatment (T2) sleep studies. Twelve patients were not restudied: 1 remitted and relocated; 9 (6 nonresponders, 3 responders) declined to participate in T2 studies; and 2 noncompliant patients were withdrawn from the protocol. Pretreatment characteristics of the study group are summarized in Table 1.

Table Graphic Jump LocationTable 1. Demographic and Pretreatment Clinical Characteristics*

Healthy normal control subjects (n=44) were studied contemporaneously with identical assessment protocols.29 Control subjects had no lifetime history of mental disorder (per the Schedule for Affective Disorders and Schizophrenia and Research Diagnostic Criteria). As it was not possible to age- and sex-match each depressed patient with a control, 2 overlapping age- and sex-equated control groups were formed for comparisons of traitlike (n=33, 14 men and 19 women; mean age, 32.8 years, SD=10.0) and state-dependent (n=27, 12 men and 15 women; mean age, 35.5 years, SD=8.9) variables.

EEG SLEEP STUDIES

All patients discontinued taking antidepressants for at least 2 weeks (6 weeks for fluoxetine hydrochloride) before baseline (T1) sleep studies. All patients were monitored prospectively for at least 14 days to ensure abstinence from alcohol and other psychoactive substances. Patients kept daily logs of sleep-related activities and were instructed not to nap before sleep studies.

Posttreatment EEG sleep studies were typically performed 16 to 20 weeks after T1. In 1 case, however, T2 studies were completed after only 6 weeks of therapy because the patient required hospitalization after a suicide attempt.

Sleep was recorded using a routine polysomnographic montage. Paper speed was 10 mm/s, and sensitivity on the EEG channel was 7.5 µV/mm. High- and low-frequency filter settings were 30.0 and 0.3 Hz, respectively, for EEG and electro-oculogram and 90 and 10 Hz, respectively, for electromyelogram. Sleep records were scored by certified polysomnographic technologists according to standard criteria.34,35 Two night means were used to lessen the impact of night-to-night variability. Excellent interrater reliability of visual scoring (ie, intraclass correlation coefficients >0.85) was maintained in our laboratory by ongoing quality assurance procedures.

The automated methods used for period analysis of delta waves and REM counts have been described in detail elsewhere.36 Four representative automated measures were used: the average delta count for the whole night, the delta ratio (a ratio of average delta counts from the first and second non-REM periods), the average REM counts for the whole night, and the average REM count for the first REM period. Four patients (2 at T1 and 2 at T2) had technically poor automated data that could not be used in the analyses.

SELECTION OF SLEEP VARIABLES

Although it is desirable to retain as much information as possible about the rich, multidimensional nature of sleep neurophysiology, univariate analyses of 20 different variables are unwieldy and increase the likelihood of false positive findings. We therefore selected a smaller number of representative sleep abnormalities, as described below.

Selection of Traitlike Variables

The typology of Kupfer and Ehlers4 was used to select 3 presumed traitlike (type 1) abnormalities: reduced REM latency (≤65 minutes),12 decreased slow wave sleep (≤8%),24 and decreased delta sleep ratio (<1.1).22 Because no single type 1 abnormality has high specificity for depression,1,2 we defined an abnormal type 1 profile on the basis of at least 2 disturbances. Thirty-seven patients (41%) met this criterion at T1. Compared with the remaining 53 patients, this subgroup had significantly reduced slow wave sleep (%) (t88=−5.59, P=.0001), REM latency (t88=−6.22, P=.0001), and delta ratio (t87=−3.19, P=.002) values.

Selection of State-Dependent Variables

Thase et al29 developed a discriminant index score based on 3 sleep measures—REM latency, REM density, and sleep efficiency—to characterize the state-dependent (type 2) sleep profiles of Kupfer and Ehlers.4 It was previously reported that patients with abnormal index scores were significantly less responsive to CBT5 and interpersonal psychotherapy,6 but not antidepressant therapy,6 compared with patients with normal profiles. The validation and psychometric characteristics of this variable are described in a separate publication.29 An optimal "cutting score" was calculated using the following simplified equation: −20.5 + (0.0519 × REM latency) − (1.61 × REM density) + (0.22 × sleep efficiency). Scores of 0 or lower identified patients with abnormal type 2 profiles; higher scores were considered normal. Forty (44%) of the 90 patients had an abnormal type 2 profile at T1. These patients differed significantly from the remainder (n=50) on 9 of 17 standard sleep measures (Table 2). Each of the differences could be attributed to either poor sleep efficiency or increased phasic REM sleep.

Table Graphic Jump LocationTable 2. Pretreatment Electroencephalographic Sleep Variables in Abnormal and Normal Type 2 Groups*

As noted earlier, reduced REM latency is considered to be both a type 1 and a type 2 variable in the model proposed by Kupfer and Ehlers.4 For example, a traitlike reduced REM latency value (eg, 55 minutes) may decrease even further (eg, 20 minutes) during a severe depressive episode. However, because few outpatients have such markedly reduced values, we predicted that REM latency would be relatively stable even when grouped with the other type 2 abnormalities. The classifications did not overlap significantly despite sharing reduced REM latency as a common disturbance: normal type 1 and normal type 2, n=32 (36%); normal type 1 and abnormal type 2, n=21 (23%); abnormal type 1 and normal type 2, n=18 (20%); and abnormal type 1 and abnormal type 2, n=19 (21%) (χ2=1.2, df=1, P=.27).

TREATMENT AND OUTCOME

The 16-week, 20-session individual therapy protocol28 began immediately after T1 sleep studies. No psychotropic medications were permitted during therapy in the acute phase. All therapists had completed 2 years or more of supervised training, and their competency was "certified" by accepted standards.37,38 Symptoms were assessed every other week by independent clinical evaluators and patient self-reporting. Remission was defined by at least 2 consecutive HRSD scores of 6 or less sustained to the end of the protocol.39 At T2, the remitted group of patients (n=43, 55%) had significantly lower scores on the HRSD (mean=2.1, SD=2.4) than the remainder (n=35; mean=9.7, SD=4.8, t76=8.47, P<.0001). The remitted group also experienced significantly greater improvement on the Beck Depression Inventory40 (t76=7.57, P<.0001) and the Global Assessment Scale41 (t76=−6.97, P<.0001).

HYPOTHESES AND STATISTICAL TESTS

Three hypotheses were tested: (1) type 1 abnormalities will persist despite remission; (2) the abnormal type 2 profile and 2 of its component variables (increased REM density and poor sleep efficiency) will normalize after treatment; and (3) remission will be associated with greater improvements of type 2 (state-dependent), but not type 1 (state-independent), sleep disturbances.

The primary analyses used a mixed-model design of nonorthogonal (correlated) main effects.42 Nonorthogonality resulted from unequal cell sizes and the fact that the abnormal type 2 profile was associated with a lower remission rate.5 A multivariate analysis of covariance (MANCOVA) was performed on the type 1 variables and an analysis of covariance (ANCOVA) was performed using the discriminant index score as a composite measure of type 2 disturbance. Age was covaried in each analysis. These analyses yielded 2 between-subject effects—remission group (remitted vs unremitted) and sleep group (abnormal at T1 vs normal at T1)— with time (T1 vs T2) as the within-subject effect. The method described by Maxwell and Delaney42 was used to interpret the potentially correlated effects. All F ratios were calculated using type III sums of squares. The 3-way interaction terms (remission group, sleep group, and time) were interpreted first. If significant, analyses would be limited to explication of the 3-way interaction, using either paired or between-group t tests. If not significant, the 2-way interactions (remission group and sleep group, remission group and time, and sleep group and time) were examined next. Main effects were interpreted only if relevant interactions were definitely not significant (P>.10).42 With harmonic mean cell sizes of 37 subjects, main effect sizes (f) of 0.33 or more were detectable with statistical power of 80% or greater (α=.05, β=.20).43

A second analysis compared the T2 sleep of the patients who remitted (normal vs abnormal profiles at T1) with that of the control groups. Again, a MANCOVA (type 1 variables) and an ANCOVA (type 2 composite score) were performed. Normalization of sleep abnormalities would result in no differences across groups at T2.

In the third analysis, the stability of the abnormal/normal classifications (from T1 to T2) were examined using Mantel-Haenszel χ2 tests. Whereas type 1 profiles were predicted to be stable, the abnormal type 2 classification was predicted to normalize at T2.

SLEEP DISTURBANCES PREDICTED TO BE STABLE (TYPE 1 ABNORMALITIES)

Consistent with the prediction of state independence, only the MANCOVA main effect for the sleep group was significant (Table 3). Univariate sleep group effects were significant on REM latency (F1,70=20.01, P<.001) and slow wave sleep (%) (F1,70=20.96, P<.001), but not delta sleep ratio (F1,70=3.48, P=.07).

Table Graphic Jump LocationTable 3. Multivariate Analysis of Type 1 (Traitlike) Sleep Abnormalities*

When the T2 sleep of the patients who remitted (abnormal, n=16; normal, n=27) was compared with that of the control group (n=33), a significant MANCOVA group effect was again observed (F6,138=3.54, P<.003). Univariate analyses confirmed this effect on all 3 variables at T2 (REM latency: F2,71=3.19, P=.05; slow wave sleep: F2,71=4.44, P=.02; delta sleep ratio: F2,71=3.11, P=.05). In each case the group with type 1 abnormalities had significantly lower values than the other 2 groups, which were indistinguishable.

The proportion of patients with abnormal type 1 profiles was almost identical at T1 (30 of 78) and T2 (29 of 78). There was highly significant concordance (ie, stability) of these classifications between time points (78% agreement; Mantel-Haenszel χ2=22.2, df=1, P<.0001).

SLEEP DISTURBANCES PREDICTED TO BE STATE-DEPENDENT (TYPE 2 ABNORMALITIES)

The ANCOVA 3-way interaction term, the time and remission group and the sleep group and remission group 2-way interactions, and the main effect for remission status were not statistically significant. There was, however, the predicted interaction of time and sleep group (Table 4). Whereas normal scores did not change significantly (t43=−1.48, P=.16), the abnormal type 2 scores increased (ie, improved) significantly (t33=5.13, P<.0001). This resulted in a highly significant between-group difference in change scores from T1 to T2 (normal group: mean=−0.37, SD=1.66; abnormal group: mean=2.02, SD=2.30; t76=5.34, P=.001).

Table Graphic Jump LocationTable 4. Analysis of the Effects of Time, Remission Group, and Sleep Group on the Type 2 Composite Variable*

A MANCOVA was next performed on the individual type 2 variables (Table 5). The overall results were identical to those using the composite score. Univariate tests revealed that the significant time and sleep group interaction was largely due to improved sleep efficiency, although there was also a trend for REM density to decrease.

Table Graphic Jump LocationTable 5. Multivariate Analysis of Type 2 (State-Dependent) Sleep Variables*

At T2, the composite scores of the patients who remitted (abnormal type 2, n=16; normal type 2, n=27) and the controls (n=27) did not differ significantly (F2,65=2.38, P=.10). However, the patients with abnormal type 2 profiles who remitted had significantly lower scores than the control group (mean=0.27, SD=1.91 vs mean=1.58, SD=2.00; t41=2.12, P=.04).

Consistent with the results described above, the proportion of patients with abnormal type 2 profiles decreased significantly from T1 (34 of 78) to T2 (22 of 78) (Mantel-Haenszel χ2=4.00, df=1, P=.05). Concordance was high (84% [37/44]) among patients with normal T1 profiles, whereas 56% (19/34) of those with abnormal scores at T1 were reclassified as normal at T2. This difference in stability was highly significant (Mantel-Haenszel χ2=7.44, df=1, P=.006).

Our study is the first prospective test of the model by Kupfer and Ehlers4 of EEG sleep disturbance in patients with depression. We found that the proposed type 1 variables—reduced REM latency, decreased delta sleep ratio, and decreased slow wave sleep (%)—were stable or traitlike. Our research design does not, of course, permit differentiation between true traits and abnormalities that may have developed as "scars" or sequelae of depressive episodes.24 However, other groups have found evidence of traitlike behavior using family studies811 and high-risk paradigms.1216 Moreover, no evidence of progressive worsening or "scarring" of type 1 variables was found in a recent study comparing first-episode and highly recurrent depressions.44

Although reduced REM latency and decreased slow wave sleep (%) were stable in this study, they are not necessarily static phenomena. For example, an age-dependent decline of both measures is well documented.4548 Reductions of REM latency and slow wave sleep (%) also can be induced by environmental49 and pharmacological50 manipulations. Therefore, multiple mechanisms are probably involved in the genesis of these sleep abnormalities. What role, then, might type 1 disturbances play in depressive vulnerability? Several lines of research document the importance of the first non-REM period for the restorative quality of sleep, processing affectively charged memories, and maintaining optimal neuropsychological performance (see, eg, Horne51). The type 1 profile also is associated with blunted nocturnal growth hormone secretion, another state-independent correlate of depression.4,52 Decreased slow wave activity during the first non-REM period also could serve as a faulty "barrier" against intrusion of the more neurophysiologically arousing REM sleep.2 Thus, people with type 1 sleep disturbances may have increased vulnerability to depression because of persistent information processing deficits or difficulties modulating affect in response to significant stressors.5,51

The incomplete normalization of the type 2 variables is not fully consistent with the predictions of the Kupfer-Ehlers model.4 Only a "thin" majority of the abnormal type 2 profiles had normalized at T2, and the remitted and unremitted groups had comparable improvements. Demonstration of a close relationship between improvement in sleep profiles and quality of remission would have been stronger evidence of state dependence. Perhaps the unremitted patients were not symptomatic enough to permit differentiation of neurophysiological parameters. Specifically, the 8-point HRSD difference between the remitted and unremitted groups at T2 is only about 40% of the magnitude that typically separates controls and untreated depressed patients. Conversely, the time between T1 and T2 studies may have been too short to permit full recovery. Differential attrition of unremitted patients with abnormal T1 studies also may have compromised the power to detect between-group differences,5 although the similarity of cell means is inconsistent with a type II error. Kraemer et al20 suggest that multiple studies across several clinical states may be necessary to completely disentangle state-dependent and traitlike abnormalities.

Is the partial normalization of type 2 disturbances clinically significant? The observed improvement in sleep efficiency has obvious beneficial effects. Most effective antidepressant therapies also suppress REM sleep,1,2 although the physiological benefit of this effect is less immediately obvious. Some evidence links increased phasic REM sleep to intensity of dysphoric affects, perhaps resulting from an imbalance of serotoninergic and cholinergic neurotransmission.2 Consistent with this notion, an earlier study showed that the reduction in phasic REM sleep following CBT was correlated with improvements in affective and cognitive symptoms.53 Buysse et al14 similarly observed a small reduction in phasic REM sleep in their study of 42 depressed patients treated with interpersonal psychotherapy. The reduction of REM sleep observed after psychotherapy, however, is much smaller than that observed with use of most antidepressant medications.1,2

Two limitations of this study warrant comment. First, the results observed in this relatively uncomplicated group of outpatients may not be fully generalizable to unselected populations. Second, without parallel pharmacotherapy or placebo groups, it is possible that the observed changes in sleep resulted from spontaneous remission or repeated testing. However, we found no effect for repeated testing in an earlier study of controls,26 and it is unlikely that a 55% remission rate is attributable to spontaneous remission.54 The predicted stability of the type 1 sleep disturbances also is not consistent with regression to the mean.

Although improvements in clinical ratings of neurovegetative symptoms are well documented, evidence that psychotherapy exerts comparable effects on more direct measures of brain function has been slow to emerge. When our current results are added to those of Baker and colleagues55,56 (ie, normalization of cerebral glucose metabolism after behavioral treatment of obsessive-compulsive disorder), Shear et al57 (ie, reduced vulnerability to lactate-induced panic attacks after CBT), and Joffe et al58 (ie, decreased thyroid hormone levels after CBT for depression), it seems probable that modern forms of psychotherapy do have significant neurobiological effects. Nevertheless, the persistence of sleep abnormalities in remitted, but unmedicated, patients may have worrisome prognostic implications.5,1214,21 In future studies we will examine whether residual sleep abnormalities convey a greater risk of relapse during naturalistic follow-up and, among patients who remain well, whether sleep profiles eventually will normalize.

Accepted for publication March 19, 1997.

This work was supported in part by grants MH-41884 (Drs Thase and Simons), MH-40023 (Drs Reynolds and Thase), MH-00295 (Dr Reynolds), and MH-30915 (Mental Health Clinical Research Center) from the National Institute of Mental Health, Bethesda, Md, as well as a grant from the John D. and Catherine T. MacArthur Foundation's Depression Network, Chicago, Ill.

We acknowledge the contributions of the staff and faculty of the Cognitive Therapy Clinic and the Sleep Evaluation Center of the Western Psychiatric Institute and Clinic, including Tim Harden, Lisa Stupar, and Christine Johnson, for their assistance in the completion of this research. Joel Greenhouse, PhD, David J. Kupfer, MD, and several anonymous reviewers also made a number of helpful suggestions about earlier drafts.

Reprints: Michael E. Thase, MD, Western Psychiatric Institute and Clinic, Department of Psychiatry, University of Pittsburgh School of Medicine, 3811 O'Hara St, Pittsburgh, PA 15213.

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Elkin  IShea  MTWatkins  JTImber  SDSotsky  SMCollins  JFGlass  DRPilkonis  PALeber  WRDocherty  JPFiester  SJParloff  MB National Institute of Mental Health Treatment of Depression Collaborative Research Program: general effectiveness of treatments. Arch Gen Psychiatry. 1989;46971- 982
Frank  EPrien  RFJarrett  DBKeller  MBKupfer  DJLavori  PRush  AJWeissman  MM Conceptualization and rationale to consensus definitions of terms in major depressive disorders: response, remission, recovery, relapse, and recurrence. Arch Gen Psychiatry. 1991;48851- 855
Beck  ATWard  CHMendelson  MMock  JErbaugh  J An inventory for measuring depression. Arch Gen Psychiatry. 1961;4561- 571
Endicott  JSpitzer  RLFleiss  JLCohen  J The Global Assessment Scale: a procedure for measuring the overall severity of psychiatric disturbance. Arch Gen Psychiatry. 1976;33766- 771
Maxwell  SEDelaney  HD Designing Experiments and Analyzing Data.  Belmont, Calif Wadsworth Publishing Co1990;
Kraemer  HCThiemann  S How Many Subjects? Statistical Power Analysis in Research.  Newbury Park, NJ Sage Publications Inc1987;
Thase  MEKupfer  DJBuysse  DJFrank  ESimons  ADMcEachran  ABRashid  KFGrochocinski  VJ Electroencephalographic sleep profiles in single-episode and recurrent unipolar forms of major depression, 1: comparison during acute depressive states. Biol Psychiatry. 1995;38506- 515
Benca  RMObermeyer  WHThisted  RAGillin  JC Sleep and psychiatric disorders: a meta-analysis. Arch Gen Psychiatry. 1992;49651- 668
Ulrich  RFShaw  DHKupfer  DJ Effects of aging on EEG sleep in depression. Sleep. 1980;331- 40
Gillin  JCDuncan  WCMurphy  DLPost  RMWehr  TAGoodwin  FKWyatt  RJBunney  WE Age-related changes in sleep in depressed and normal subjects. Psychiatry Res. 1981;473- 78
Lauer  CJRiemann  DWiegand  MBerger  M From early to late adulthood changes in EEG sleep of depressed patients and healthy volunteers. Biol Psychiatry. 1991;29979- 993
Mullen  PELinsell  CRParker  D Influence of sleep disruption and calorie restriction on biological markers for depression. Lancet. 1986;21051- 1055
Gillin  JCMendelson  WSitaram  NWyatt  RJ The neuropharmacology of sleep and wakefulness. Annu Rev Pharmacol Toxicol. 1978;18563- 579
Horne  JA Human sleep loss and behavior implications for the prefrontal cortex and psychiatric disorder. Br J Psychiatry. 1993;162413- 419
Jarrett  DBMiewald  JMKupfer  DJ Recurrent depression is associated with a persistent reduction in sleep-related growth hormone secretion. Arch Gen Psychiatry. 1990;47113- 118
Nofzinger  EASchwartz  RMReynolds  CF  IIIThase  MEJennings  RJFrank  EFasiczka  ALGaramoni  GLKupfer  DJ Affect intensity and phasic REM sleep in depressed men before and after treatment with cognitive behavior therapy. J Consult Clin Psychol. 1994;6283- 91
Depression Guideline Panel, Depression in Primary Care: Vol. 2 Treatment of Major Depression, Clinical Practice Guideline Number 5.  Rockville, Md Agency for Health Care Policy and Research1993;Publication 93-0551.
Baxter  LC  JrSchwartz  JMBergman  KSSzuba  MPGuze  BHMazziotta  JCAlazraki  ASelin  CEFerng  HKMunford  PPhelps  ME Caudate glucose metabolic rate changes with both drug and behavior therapy for OCD. Arch Gen Psychiatry. 1992;49681- 689
Schwartz  JMStoessel  PWBaxter  LRMartin  KMPhelps  ME Systematic changes in cerebral glucose metabolic rate after successful behavior modification treatment of obsessive-compulsive disorder. Arch Gen Psychiatry. 1996;53109- 113
Shear  MKFyer  AJBall  GJosephson  SFitzpatrick  MGitlin  BFrancis  AJGorman  JLiebowitz  MKlein  DF Vulnerability to sodium lactate in panic disorder patients given cognitive behavioral therapy. Am J Psychiatry. 1991;148795- 797
Joffe  RSegal  ZSinger  W Change in thyroid hormone levels following response to cognitive therapy for major depression. Am J Psychiatry. 1996;153411- 413

Figures

Tables

Table Graphic Jump LocationTable 1. Demographic and Pretreatment Clinical Characteristics*
Table Graphic Jump LocationTable 2. Pretreatment Electroencephalographic Sleep Variables in Abnormal and Normal Type 2 Groups*
Table Graphic Jump LocationTable 3. Multivariate Analysis of Type 1 (Traitlike) Sleep Abnormalities*
Table Graphic Jump LocationTable 4. Analysis of the Effects of Time, Remission Group, and Sleep Group on the Type 2 Composite Variable*
Table Graphic Jump LocationTable 5. Multivariate Analysis of Type 2 (State-Dependent) Sleep Variables*

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Doman  JDetka  CHoffman  TKesicki  DMonahan  JPBuysse  DJReynolds  CFCoble  PAMatzzie  JKupfer  DJ Automating the sleep laboratory: implementation and validation of digital recording and analysis. Int J Biomed Comput. 1995;38277- 290
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Elkin  IShea  MTWatkins  JTImber  SDSotsky  SMCollins  JFGlass  DRPilkonis  PALeber  WRDocherty  JPFiester  SJParloff  MB National Institute of Mental Health Treatment of Depression Collaborative Research Program: general effectiveness of treatments. Arch Gen Psychiatry. 1989;46971- 982
Frank  EPrien  RFJarrett  DBKeller  MBKupfer  DJLavori  PRush  AJWeissman  MM Conceptualization and rationale to consensus definitions of terms in major depressive disorders: response, remission, recovery, relapse, and recurrence. Arch Gen Psychiatry. 1991;48851- 855
Beck  ATWard  CHMendelson  MMock  JErbaugh  J An inventory for measuring depression. Arch Gen Psychiatry. 1961;4561- 571
Endicott  JSpitzer  RLFleiss  JLCohen  J The Global Assessment Scale: a procedure for measuring the overall severity of psychiatric disturbance. Arch Gen Psychiatry. 1976;33766- 771
Maxwell  SEDelaney  HD Designing Experiments and Analyzing Data.  Belmont, Calif Wadsworth Publishing Co1990;
Kraemer  HCThiemann  S How Many Subjects? Statistical Power Analysis in Research.  Newbury Park, NJ Sage Publications Inc1987;
Thase  MEKupfer  DJBuysse  DJFrank  ESimons  ADMcEachran  ABRashid  KFGrochocinski  VJ Electroencephalographic sleep profiles in single-episode and recurrent unipolar forms of major depression, 1: comparison during acute depressive states. Biol Psychiatry. 1995;38506- 515
Benca  RMObermeyer  WHThisted  RAGillin  JC Sleep and psychiatric disorders: a meta-analysis. Arch Gen Psychiatry. 1992;49651- 668
Ulrich  RFShaw  DHKupfer  DJ Effects of aging on EEG sleep in depression. Sleep. 1980;331- 40
Gillin  JCDuncan  WCMurphy  DLPost  RMWehr  TAGoodwin  FKWyatt  RJBunney  WE Age-related changes in sleep in depressed and normal subjects. Psychiatry Res. 1981;473- 78
Lauer  CJRiemann  DWiegand  MBerger  M From early to late adulthood changes in EEG sleep of depressed patients and healthy volunteers. Biol Psychiatry. 1991;29979- 993
Mullen  PELinsell  CRParker  D Influence of sleep disruption and calorie restriction on biological markers for depression. Lancet. 1986;21051- 1055
Gillin  JCMendelson  WSitaram  NWyatt  RJ The neuropharmacology of sleep and wakefulness. Annu Rev Pharmacol Toxicol. 1978;18563- 579
Horne  JA Human sleep loss and behavior implications for the prefrontal cortex and psychiatric disorder. Br J Psychiatry. 1993;162413- 419
Jarrett  DBMiewald  JMKupfer  DJ Recurrent depression is associated with a persistent reduction in sleep-related growth hormone secretion. Arch Gen Psychiatry. 1990;47113- 118
Nofzinger  EASchwartz  RMReynolds  CF  IIIThase  MEJennings  RJFrank  EFasiczka  ALGaramoni  GLKupfer  DJ Affect intensity and phasic REM sleep in depressed men before and after treatment with cognitive behavior therapy. J Consult Clin Psychol. 1994;6283- 91
Depression Guideline Panel, Depression in Primary Care: Vol. 2 Treatment of Major Depression, Clinical Practice Guideline Number 5.  Rockville, Md Agency for Health Care Policy and Research1993;Publication 93-0551.
Baxter  LC  JrSchwartz  JMBergman  KSSzuba  MPGuze  BHMazziotta  JCAlazraki  ASelin  CEFerng  HKMunford  PPhelps  ME Caudate glucose metabolic rate changes with both drug and behavior therapy for OCD. Arch Gen Psychiatry. 1992;49681- 689
Schwartz  JMStoessel  PWBaxter  LRMartin  KMPhelps  ME Systematic changes in cerebral glucose metabolic rate after successful behavior modification treatment of obsessive-compulsive disorder. Arch Gen Psychiatry. 1996;53109- 113
Shear  MKFyer  AJBall  GJosephson  SFitzpatrick  MGitlin  BFrancis  AJGorman  JLiebowitz  MKlein  DF Vulnerability to sodium lactate in panic disorder patients given cognitive behavioral therapy. Am J Psychiatry. 1991;148795- 797
Joffe  RSegal  ZSinger  W Change in thyroid hormone levels following response to cognitive therapy for major depression. Am J Psychiatry. 1996;153411- 413

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