Conditions: Major Depressive Disorder
Keywords: Major Depressive Disorder (MDD) Cognitive Behavioral Therapy (CBT) Escitalopram Quantitative EEG (QEEG)
Allocation: RANDOMIZED | Intervention model: PARALLEL
Enrollment: 39
Healthy volunteers: no

Brief summary:
The purpose of this research study is to find out if a test can predict whether someone with depression will get better with treatment. We also want to find out whether there are changes in the brains of depressed patients having different types of treatment (drug therapy vs. talk therapy). We hope that a test called QEEG (Quantitative Electroencephalogram) can tell us if a treatment is going to work, even before the person starts to feel better.

Hypothesis 1: Response to treatment will correlate with changes in QEEG metrics.

Hypothesis 2: QEEG parameters, different from those that predict response to pharmacotherapy, will be associated with response to CBT.

Detailed description:
To our knowledge, QEEG has not been studied in the prediction of response to CBT, an important and widely used non-pharmacologic approach to treating depression. Establishing QEEG technology as a predictor of response to CBT could help to guide treatment selection for individual patients. It is probable that certain patient populations are more likely to respond to either psychotherapeutic or psychopharmacological interventions, while others may benefit from a combination of treatment modalities. This study will provide preliminary information about the utility of QEEG as a predictor of response in psychotherapy and will furnish the knowledge base of QEEG changes related to clinical variables, providing pilot data for a study in a larger sample. We have hypothesized that clinical response will correlate with changes in QEEG metrics from beginning to two weeks after treatment, and that QEEG parameters, different from those that predict response to pharmacotherapy, will be associated with response to CBT.

Arms:
arm 1: CBT is a manualized therapeutic treatment for depression based on principles of cognitive restructuring and behavioral changes. arm 2: Escitalopram or Lexapro is a Selective Serotonin Reuptake Inhibitor (SSRI) used to treat depression

Interventions: intervention 1: Cognitive Behavioral Therapy (CBT) intervention 2: Escitalopram
Intervention descriptions: intervention 1: The CBT group will receive weekly 50-minute individual sessions over the course of 12 weeks conducted by experienced therapists who are trained in manual based CBT. intervention 2: The medication group will receive open label treatment of escitalopram, 10-20 mg/day, flexible dose, for 12 weeks, and will be seen every two weeks by a study physician.

Protocol document:
Prot_000.pdf: 
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QEEG Predictors of Response for Psychotherapy Compared to Pharmacotherapy in 
Depression 
 
Principal Investigator: Amy Farabaugh, PhD 
 
 
I. Background and significance 
 
A.  Treatment of MDD: 
  
Over two-thirds of patients with MDD treated with first-line medications, such as 
citalopram, fail to achieve remission during 12 weeks of acute treatment. Patients who fail to 
achieve remission typically have greater impairment of social and work function and a much 
higher risk of relapse and recurrence of MDD than those who remit (Paykel et al., 2005). As 
demonstrated in the STAR*D trial, many patients who go on to achieve remission on 
pharmacotherapy or CBT do not do so until the second 6 weeks of treatment even with enhanced, 
measurement-based care (Rush et al., 2006).  
 
Recently, an increasing body of “predictor studies” has emerged which aims to use 
neuroimaging measures to predict treatment outcomes. These studies are pivotal in advancing 
treatment options for depression, as validated surrogate markers that predict response at baseline 
or soon after starting treatment would help clinicians optimize a patient’s treatment within the 
first several weeks of initiation, potentially saving weeks to months of ongoing distress, disability 
and risk of suicide. In light of the challenges that MDD poses to clinicians and patients alike, 
identifying biomarkers which are able to predict outcome are urgently needed to help further 
refine the standard of care for depression.  Additionally, to our awareness no studies have used 
neuroimaging measures to directly compare different psychosocial interventions, such as 
Cognitive Behavioral Therapy (CBT).   
 
 
B. Functional Brain Changes Related to Response to CBT:  
 
Functional brain imaging has suggested the possibility that pharmacotherapy and 
psychotherapy, while both representing effective treatments for MDD, may exert their effects 
through different mechanisms (Goldapple et al., 2004).  CBT is seen as having a “top down” 
effect, while pharmacotherapy is viewed as generating a “bottom up” neurobiological cascade.  
Despite the strong evidence base and widespread use of CBT for depression, we still know very 
little about its biological mechanisms of action, and even less about biological predictors of 
response to CBT.  The identification of early predictors of response using QEEG might further 
our understanding of the mechanisms underlying the efficacy of CBT in MDD.  
 
 
 
C. QEEG:  
Quantitative electroencephalography (QEEG) has emerged in recent years as a 
technology that may help predict eventual response to antidepressants as early as the first week of 
prescription and may help shed light on the mechanism(s) of action of depression treatments. 
QEEG has enabled spectral analysis of EEG signals using a digitized signal on magnetic or 
optical media, providing information that cannot be extracted through visual inspection of the 
traditional EEG paper tracings. Cordance, a QEEG measure integrating absolute and relative 
power of the signal, was shown to be decreased in subjects with MDD, compared to normal 


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subjects (Cook et al, 1998).  Researchers have reported differences between responders and non-
responders to antidepressant treatment with respect to the absolute or relative power of the EEG 
signal (Ulrich et al., 1994; Knott et al, 1996). Subsequently, QEEG has been shown to have utility 
in predicting clinical response to SSRI antidepressants (Cook et al, 1999). Specifically, in a case 
series, Cook and Leuchter (2001) found that clinical response to antidepressant medications was 
preceded by decreases in QEEG measurements (theta cordance) in the prefrontal brain regions.  
 
D. Reward Responsiveness Task 
This study will also utilize the Signal Detection computer task which measures 
anhedonia, the loss of pleasure or lack of reactivity to pleasurable stimuli (APA 1994), and has 
been considered a trait marker related to vulnerability for depression (Loas 1996; Meehl 1975).   
Depression has been found to be associated with low levels of self-reinforcement (Gotlib 1982);  
diminished emotional response to pleasant cues (Berenbaum and Oltmanns 1992; Sloan et al  
2001); underestimation of the frequency of positive reinforcements received (Buchwald 1977);  
decreased responsiveness to reward (Hughes et al 1985); and decreased spatial attention to  
positive but not negative facial expression (Suslow et al 2001). Previous behavioral studies  
indicate that depressed individuals and subjects with elevated depressive symptoms show altered  
reward processing as compared with controls, but normal punishment processing (Henriques &  
Davidson, 2000).  The Signal Detection Task has been implemented in a non-depressed sample as 
well as schizophrenic samples and has shown that increased levels of depressive symptoms are  
associated with decreased responsiveness to reward cues.  
 
E. Summary and Rationale: 
Studies which identify validated surrogate markers at baseline or soon after starting 
treatment that can predict response to treatment have the potential to refine the standard of care 
for depression, as these predictor studies will reduce the amount of time that clinicians spend 
adjusting treatments to find the most optimal treatment option.  Additionally, studies which 
compare predictive biomarkers between treatments, particularly between psychotherapy and 
pharmacotherapy, will further enable clinicians to choose an optimal treatment.    
To our knowledge, QEEG has not been studied in the prediction of response to CBT, an 
important and widely used non-pharmacologic approach to treating depression.  Establishing 
QEEG technology as a predictor of response to CBT could help to guide treatment selection for 
individual patients.  It is probable that certain patient populations are more likely to respond to 
either psychotherapeutic or psychopharmacological interventions, while others may benefit from 
a combination of treatment modalities.  The public health significance of a valid technology that 
provides an objective means of determining likelihood of response to treatment would be 
enormous. This study will provide preliminary information about the utility of QEEG as a 
predictor of response in psychotherapy and will furnish the knowledge base of QEEG changes 
related to clinical variables, providing pilot data for a study in a larger sample. 
Additionally, very few studies have assessed changes in reward responsiveness as a 
function of treatment. It is possible that CBT and pharmacological interventions have different 
effects on reward responsiveness and anhedonia, and thus the use of this task will allow us to 
investigate the influence of different treatments on reward processing in this population. We 
expect that the results of this study will guide future research with the ultimate goal of identifying 
a behavioral indicator of underlying dysfunction in reward related brain regions. Finally, it is 
possible that the reward responsiveness will act as a predictor of response to both psychotherapy 
and pharmacological treatment. 
 
II. Specific Aims: 
 


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Primary Aim 1: To determine whether QEEG technology can predict whether a patient with 
MDD will respond to CBT.   
Hypothesis 1: Clinical response (defined as >50% change in HAMD-17 from beginning to 
endpoint) will correlate with changes in QEEG metrics (ATR, EEG Bispectrum, cordance 
estimates) from beginning to two weeks after starting treatment. 
 
Primary Aim 2: To determine whether there are unique biological changes in patients undergoing 
CBT, as measured by the QEEG, that are different than those associated with antidepressants 
(SSRIs).   
Hypothesis 2:  QEEG parameters, different from those that predict response to pharmacotherapy, 
will be associated with response to CBT. 
 
Secondary Aim 1: To determine whether there are correlations between QEEG patterns and 
changes in clinical functioning, assessed by clinical and self-rated instruments (SF-36, Symptom 
Questionnaire, Beck Depression Inventory), in both arms (CBT vs. escitalopram).   
 
Secondary Aim 2: To determine whether there are any correlations between QEEG metrics and 
clinical variables (gender, age, comorbid anxiety, chronicity of depression).   
 
Secondary Aim 3: To determine whether the behavioral processes associated with reward 
responsiveness in a depressed population change as a function of treatment.   
 
 
III. Subject Selection 
 
A. Inclusion/exclusion criteria: 
 
   Inclusion Criteria:   
1. Subjects will be adults, ages 18 to 75 years. 
2. Written informed consent 
3. MDD, current according to the fourth version of the Diagnostic and Statistical Manual for 
Mental Disorders (DSM-IV)  
4. 17-item Hamilton Depression Rating Scale (HAM-D-17) score of  > 14 at screen. 
5.  Subjects who are not currently taking any antidepressant or other psychotropic  
medications and who have been free of these medications for 4 weeks prior to screening 
visit. 
 
Exclusion Criteria: 
1. Women who are pregnant, lactating, or planning a pregnancy during the study.  
2. Women of child bearing potential who are not using a medically accepted means of  
contraception (to include oral contraceptive or implant, condom, diaphragm, spermicide, 
intrauterine device, tubal ligation, or a partner with vasectomy).  
3. Any uncontrolled psychiatric disorder. 
4. Current use of psychotropic medications: including lithium, benzodiazepine sedatives 
(such as diazepam, lorazepam, alprazolam, or clonazepam), other sedatives (such as 
buspirone, zolpidem, and zaleplon), anti-seizure medications (such as valproic acid, or 
carbamazepine), 
5. Current use of other exclusionary medications: beta-blockers (such as metoprolol or 
propranolol), dehydroepiandrosterone (DHEA), entacapone, hydroxytryptophan (5-HTP), 
ginkgo, St. John’s wort (hypericum), and warfarin (Coumadin). 


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6. Psychotic features in the current episode or a history of psychotic features. 
7. Alcohol or substance abuse or dependence within the past three months. 
8. History of head trauma or seizure disorder. 
9. History of intolerance of the study medication. 
10. Failure to respond to escitalopram up to 20 mg for at least 6 weeks. 
11. Failure to respond to 2 or more adequate antidepressant trials (6 weeks or longer on a 
therapeutic dose, equivalent to fluoxetine 40mg) in the current episode. 
12. Currently enrolled in other depression-focused psychotherapy and unwilling to cease 
treatment. 
13. Subjects who, per clinical judgment, are not appropriate candidates for CBT or SSRIs. 
14. History or current diagnosis of the following DSM-IV psychiatric illness: organic mental 
disorder, schizophrenia, schizoaffective disorder, delusional disorder, psychotic disorders 
not otherwise specified, bipolar disorder, patients with mood congruent or mood 
incongruent psychotic features, patients with substance dependence disorders, including 
alcohol, active within the last 3 months. 
15. Serious suicide or homicide risk, as assessed by the evaluating clinician or a score of 4 on 
the third item of the HAM-D. 
16. Serious or unstable medical illness, including cardiovascular, hepatic, renal, respiratory, 
endocrine, neurologic or hematologic disease 
 
B. Source of Subjects and Recruitment Methods: 
 
Patients will be recruited per usual methods, including IRB-approved advertising in newspapers, 
television and radio, and referrals from other clinicians.   
 
Patients of all ethnic and racial categories will be included.  
 
Children and adolescents under age 18 will not be included.  Children under age 18 will not be 
included.  There are limited data available about the efficacy and safety of escitalopram as an 
antidepressant in children; it would therefore be preferable to obtain more adult data prior to 
testing this treatment in children. Also, the investigators of the Depression Clinical and Research 
Program have limited experience in pediatric work, and are not board-certified in the treatment of 
pediatric populations. We also are not trained in the use of the diagnostic instruments necessary 
for the appropriate assessment of younger children with depression, which differ from those 
instruments used in adults and older adolescents. As there are many registered antidepressants 
available for use in depressed children, exclusion of young children from this study should not 
prevent them from obtaining appropriate, effective treatment with proven agents. 
 
 
IV. Subject Enrollment 
   
A. Methods of enrollment/registration/randomization: 
 
Patients are usually screened by telephone to determine tentative eligibility for studies.  The 
subjects then are invited to come to MGH for a full psychiatric diagnostic interview.  Patients 
accepted into the study will be registered through the Depression Clinical and Research Program.  
Randomization numbers will be provided by the study Biostatistician and will be maintained by 
the research pharmacists at MGH Research Pharmacy, who will administer the open label 
escitalopram, 10-20 mg/day, to those subjects who have been randomized to receive medication. 
The randomization will be 1:1, i.e. half of the patients will be randomized to receive escitalopram 
(10-20 mg/day, flexible dose) and half will be randomized to receive twelve weekly 50-minute 


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Cognitive Behavioral Therapy sessions.  Both treatment interventions have been strongly 
validated by the literature, and are considered standard of care for this population. 
 
 
V. Study Procedures 
 
a) Study visits and parameters to be measured: 
 
The study involves the enrollment of 30 outpatients with major depressive disorder.  
 
Patients screened for the study and found to be eligible will return for their baseline visit after one 
week, at which eligible patients (n = 30) will be randomized to either CBT or to standard 
pharmacotherapy with an SSRI (escitalopram 10-20mg) for 12 weeks.  
 
The CBT group will receive twelve weekly 50-minute individual sessions over the course of 12 
weeks conducted by experienced therapists who are trained in manual based CBT.  The 
medication group will receive open label treatment of escitalopram, 10-20 mg/day, flexible dose, 
for 12 weeks, and will be seen every two weeks by a study physician.  In both arms, study 
assessors, who are blind to treatment condition, will assess patients monthly (week 0, 4, 8, and 
12), and are responsible for the primary outcome measure (HAMD-17). 
 
The QEEG will be administered 5 times during the study (week 0, 1, 2, 8 and endpoint) allowing 
us to assess whether there are any correlations between QEEG patterns and changes in clinical 
functioning.  We will record EEG from a fronto-parieto-temporal derivation using a 4-channel 
EEG Monitor (Aspect Medical Systems) connected to the subject via disposable EEG electrodes.  
The adhesive material of the electrodes is similar to that found in ECG electrodes.  All recordings 
will be analyzed off-line to generate QEEG metrics.  Once such QEEG metric, the ATR, is a 
complex index derived from left-right asymmetry of combined theta+alpha (4-12Hz) power, 
relative theta powe4r, and percent change from baseline in relative theta power, and ranges from 
0 (low probability of response) to 100 (high probability of response).  De-identified data will be 
shared with Aspect Medical Systems for offline calculation of EEG-based metrics. 
 
The Signal Detection Task will be administered at baseline and at endpoint. In the specific Signal 
Detection Task we will use, called the Face Game (assessing reward sensitivity), we will present 
simple cartoon faces (diameter: 25 mm; eyes: 7 mm) in the center of the monitor and at the 
beginning of the trial. The face has no mouth. After a given delay, either a straight mouth of 11.5 
mm (“short mouth”) or 13 mm (“long mouth”) will be presented for 100 ms. Subjects will be 
instructed to press an appropriate button to decide whether a long or short mouth had been 
presented. For the present study, the experiment will consist of 300 trials, divided into 3 blocks of 
100. Long and short mouths will be presented equally often. A trial will begin with the 
presentation of an asterisk for 500 ms that will serve as fixation point and warning stimulus that a 
trial will start. After 500 ms, a mouth-less face will be presented and will stay on the screen until 
a response is made; after an inter-stimulus interval of 500 ms, one of the two mouths will be 
presented for 100 ms. Critically, not all correct responses will receive feedback (only positive 
feedback will be provided). For each block, 40 correct trials will be followed by a reward 
feedback (“Correct!! You won 5 cents”), presented for 1750 ms immediately after the correct 
response. For half of the subjects, correct identification of the little mouth will be associated with 
three times more positive feedback (30/40) than correct identification of the big mouth (10/40). 
For the other half of the subjects, the contingencies will be reversed. After feedback, an inter-trial 
interval (ITI) of 250 ms will follow. Subjects will be instructed that the aim of the study is to win 
as much money as possible, and that they will receive the amount of money won at the end of 


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experiment (actual money will be shown to the subjects for increasing the credibility of the 
experiment).  
 
Scales: 
Once the patient has agreed to participate in the study by signing the informed consent, the 
following instruments will be administered by a study assessor who is blind to treatment 
condition at screen, baseline, and visits 4, 8 and 12:   
 
a. Structured Clinical Interview for DSM-IV (Spitzer et al, 1989).  The SCID-I/P, 
administered by the clinician, proceeds by modules to the different Axis I disorders.  
Question here are asked exactly as written, and each is based on the individual 
criteria from DSM-IV. These scales will rely on retrospective reporting from the 
participant (screen visit only) 
 
b. 28-item Hamilton Rating Scale for Depression (HAM-D 28-item) (Hamilton, 1960; 
1967;Williams 1988):  This version allows scoring of the HAM-D-17, 21-, 25,and 
28-item scales.  This instrument is completed by the clinician based on his/her 
assessment of the patient’s depressive symptoms, using a structured interview and 
defined anchor points.  The HAM-D aims to quantify the degree of depression in 
patients who already have a diagnosis of major depression. These scales will rely on 
retrospective reporting from the participant. 
 
c.    BDI (Beck & Steer, 1987):  The Beck Depression Inventory is a widely used 21-item  
self-report measure that assesses the severity of affective, cognitive, and vegetative 
symptoms of depression experienced by patients over the past week.  
 
 
d.    CGI-S and I (Guy, 1976):  This measure, based on history and scores on other  
  
       instruments: a) CGI-S (severity): the current condition of the patient on a scale of 1-7  
(1 being normal, and 7 being among the most severely ill patients); b) CGI-I 
(improvement): the degree of improvement, as perceived by the clinician, since the 
start of treatment on a scale of 1-7. 
 
e.    Kellner’s Symptoms Questionnaire (SQ) (Kellner, 1987).  This instrument is  
completed by the patient based on his/her assessment of the severity of anxiety, 
depression, somatic symtpoms, and hostility. 
 
f.    Quality of Life Enjoyment and Satisfaction Questionnaire (Q-LES) (Endicott,  
      Nee, Harrison, Blumenthal, 1993). This self-report measure is a 16 item    
      sensitive measure of the degree of enjoyment and satisfaction experienced by    
      subjects in various areas of daily functioning. The summary scores have been  
      found to be realiable and valid measures of these dimensions in a group of  
      depressed outpatients.  
 
g.  DAS (Weissman & Beck, 1978):  The Dysfunctional Attitude Scale is a 40 item self-
report questionnaire that is designed to measure the presence of depressotypic 
underlying assumptions, or dysfunctional beliefs.  It is the most widely used 
instrument for this purpose.   
 
h.  ATQ-R (Kendall, Howard, & Hays, 1989):  The Automatic Thoughts Questionnaire-
Revised is a self-rated instrument to assess cognitive self-statements related to 


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depression. It includes 30 negative self-statements and 10 self-statements reflecting 
positive affect.   
 
i.  PSS (Cohen et al., 1983):  The Perceived Stress Scale is a self-rated instrument to 
evaluate a patient's level of perceived stress. Patients are asked to endorse to what 
degree, over the last month, they felt stressed.  Each of the 14 statements presented 
describes a stressful state.   
 
j.  SF36 (Ware, 1993): Medical Outcomes Study (MOS) 36 Item Short-Form Health 
Survey is a clinician-administered instrument to assess quality of life. It has 36 items 
and contains subscales that measure physical functioning, physical role functioning, 
social functioning, emotional role function, general health, energy, mental health, and 
bodily pain over the past four weeks.   
 
k. Snaith-Hamilton Pleasure Scale (SHAPS) (Snaith et al., 1995). This short 14-item 
self rated scale assesses self-reported anhedonia.  
 
Adverse events and changes in concommitent medications will be assessed at each 
visit with the clinician for subjects randomized to the medication arm. The clinician 
will inquire about any changes in dosages of medications as well as days the 
medications were not taken. The importance of taking medications as prescribed and 
refraining from taking a « holiday » from medication on weekends or at any other 
time during the study will be emphasized from the time the patient enrolls in the 
study.  
 
For subjects randomized to psychotherapy, adverse events will be assessed by the 
CBT therapist at baseline and weeks 2, 4, 6, 8, 10 and 12. 
 
Safety Data 
The following laboratory tests will be performed at the Screen Visit for only those patients 
randomized to the medication arm (with the exception of toxicology and pregnancy test, see 
below):  
a.    Complete Blood Count with differential (at week 4 as well) 
b.     Urinalysis  
c.      Clinical Chemistry (serum concentrations of electrolytes, BUN, creatinine, SGOT,  
SGPT, alkaline phosphatase, total bilirubin, albumin, total protein, uric acid, 
glucose, and cholesterol)  
d.      Electrocardiogram (EKG)  
e.      Urine pregnancy Test (For patients in both arms, at Screen and Week 12 or  
                   endpoint)  
f.       Toxicology (patients in both arms will be screened for drugs of abuse)  
 
The routine laboratory tests (complete blood count, urinalysis, clinical chemistry tests) will be 
performed by Massachusetts General Hospital Clinical Laboratories. Significantly abnormal 
laboratory values will be those falling outside of the normal range for the laboratory and deemed 
to be clinically significant by the study physicians.  
 
For patients in both groups, weight, oral temperature, and standing and supine pulse and blood 
pressure (Vital Signs) will be recorded at the screen visit (and at every visit thereafter for patients 
in the medication arm. For patients in both groups, a physical exam will be performed at screen. 
 


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In the case of early termination, patients in both groups will be asked to return for a final study 
visit within 48 hours of leaving the study.  All Week 12 study procedures will be completed at the 
time of the early termination visit.  
 
b) Drugs to be used: 
 
Patients randomized to receive escitalopram will receive open-label treatment with 
escitalopram, 10-20 mg/day, flexible dose, for 12 weeks. Dose selections will be determined 
by the study physician.    
 
c)    Devices to be used: 
       None. 
 
d)   Procedures: 
       None. 
 
 
VI. Statistical Analysis 
 
a) 
Statistical Plan: The Mann-Whitney U test will be used to analyze differences in changes 
of QEEG between responders and non-responders to CBT.  We will use logistic 
regression with the outcome being response/no response on the HAMD-17 and the 
predictors being other EEG measures. We will test the predictors in a stepwise manner 
given the limited power with a small N.  A power calculation based on a previous QEEG 
study conducted in our clinic reveals that, to achieve power = 0.80, we would need 34 
subjects per group. With N = 30 per group (60 subjects total), our power is 0.76. Power 
for the current study, with N = 15 per group, is 0.47. While this study is underpowered 
due to the small N, the goal of this study is to act as a pilot study which can help measure 
effect sizes, providing a foundation for future, larger projects.   
 
For secondary aims, we will use Pearson r’s for pairs of continuous measures, or point-
biserial r for pairs of dichotomous and continuous measures. 
 
 
Data from the Signal Detection Fact Task will be analyzed by Dr. Diego Pizzagalli at 
Harvard University. Response bias will be the main variable of interest, whereas reaction 
time, hit rates, and discriminability values will provide control variables. Analyses of 
variance (ANOVAs) will be used to investigate variables across the three blocks. 
Discriminability and response biases will be computed using standard formulae (e.g. 
McCarthy and Davidson 1981).  
 
 
b)  
Study Endpoints:  Endpoints will be defined as the final study visit at the end of the 12 
weeks, or the termination/final visit for patients who choose to (or have to) end 
participation in the study prematurely.  
 
 
 
VII. Risks and Discomforts 
 
a) Procedures:  None performed 
 


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b)   Cognitive Behavioral Therapy: Risks and discomforts associated with receiving  
psychotherapy are generally considered modest, but can include a worsening of 
depressive and/or anxiety symptoms, and psychological discomfort associated with 
discussion of one’s difficulties.  Answering detailed questionnaires may create a mild 
degree of inconvenience for the subjects and coming in for CBT visits and blind assessor 
visits may be seen as time consuming and inconvenient. Patients will be given telephone 
numbers of the doctors involved in the study if they would like to talk about any 
discomforts. A general risk associated with treatment for depression includes the risk that 
depression may not respond to treatment.  Subjects will be informed of these risks. 
 
c)  Drug:  Escitalopram has been used in over 1 million patients in the US and there is a 
large well-validated safety database for it. Most common side-effects include nausea, 
ejaculatory problems, diarrhea and insomnia. The risks posed to the subjects who 
participate in this study are real but small.   
 
d) QEEG: There may be some initial forehead redness after removal of the 
electrodes.  This is temporary and considered normal.  There are no known long-
term effects of recording brain EEGs. All information associated with study 
participants that is shared with Aspect Medical Systems will be treated 
confidentially 
 
e)  Signal Detection Computer Task: We do not foresee any risks or discomforts presented 
by this task. 
 
f)   Device:  Not applicable 
 
g) Radiation:  None 
 
 
 
VIII. Potential Benefits 
 
a) Potential benefit to individuals:  The study will potentially provide relief of depressive 
symptoms to participating patients. Subjects will also be compensated $25 per QEEG 
($125 total for all 5 QEEGs), and will receive $20 plus earnings per trial of signal 
detection ($40 plus earnings in total).  Subjects will also be offered up to $25 travel 
reimbursement per visit. 
 
 
b) Potential benefit to society:  This proposal investigates QEEG in the prediction of 
response to both CBT and escitalopram in patients with major depressive disorder. 
Establishing QEEG technology as a predictor of response to therapy and/or medications 
could help guide treatment selection for individual patients. Thus, the study will 
potentially further our understanding of the neurobiological differences which underly 
individual differences in responses to certain treatments. The study would be one of the 
first comparisons of baseline biomarkers between patients who respond to CBT versus 
those who respond to an SSRI, thus filling an important gap in our knowledge base of 
predictors of response to different treatments.  Establishing QEEG as an accurate 
predictive tool would have an enormous impact on providing better care to patients 
suffering from major depressive disorder.  
 


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IX. Monitoring and Quality Assurance 
 
   a) Independent Monitoring of Source Data:  Documentation of the presence of any side-effect  
or adverse event will be completed at every visit and will be reported in accordance with AE 
reporting guidelines of the MGH IRB.  Patients will be encouraged to contact the investigator 
or a member of her staff at any time between visits concerning adverse events or worsening 
of symptoms.  An event that is deemed serious will be recorded in the patient’s study binder 
and will be handled in an expeditious manner.  A Safety Monitoring Committee, including 
Drs Fava, Alpert, and Farabaugh, will review SAEs quarterly.  Research assistants 
responsible for data collection and storage will be aware of and comply with all regulatory 
requirements related to adverse events.  In the event that a patient becomes ill or is injured as 
a direct result of study participation, medical care will be made available.  All adverse events 
will be followed to resolution and reported to the MGH IRB as serious in the event that they 
are unanticipated, possibly related to the study, and meet any one of the following criteria: 
 
1. Fatal or life threatening 
2. Requires prolonged inpatient hospitalization 
3. Results in persistent or significant disability or incapacity 
4. Congenital anomaly 
5. Medical events that require intervention to prevent serious outcome 
6. Cancer 
7. Overdose 
8. Results in substance dependency or abuse 
 
Outcomes Monitoring:  Treatment outcomes will be documented so that at study completion 
appropriate recommendations can be made for further treatment. 
 
 
 b) Safety Monitoring:   
 
Monitoring of Safety Data by the DSMB: 
 
1. Unblinded Reporting – Safety information for this study will be reported to the DSMB in an 
unblinded manner.  A statistical penalty will not be assessed for the ongoing unblinded review of 
safety by the DSMB. Unblinded data will not be released to the investigators unless necessary for 
safety reasons. 
 
2. Range of Safety Reporting to the DSMB – It is considered necessary for the purpose of 
monitoring the safety of the study that the DSMB review not only adverse events (AEs) and 
serious adverse events (SAEs), but other data that may reflect differences in safety between 
treatment groups.  This includes treatment retention rates, and reasons for drop-out, and 
laboratory values reflecting potential toxicity. 
 
3. Serious Adverse Events – Expedited review will occur for all events meeting the FDA 
definition of Serious Adverse Events (SAEs) – i.e., any fatal event, immediately life-threatening 
event, permanently or substantially disabling event, event requiring or prolonging inpatient 
hospitalization, or any congenital anomaly. This also includes any event that a study investigator 
or the DSMB judges to impose a significant hazard, contraindication, side effect, or precaution.  


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For purposes of this study, all SAEs will be required to be reported to the DSMB, regardless of 
any judgment of their relatedness to the study drug. All relevant information will be reported to 
the DSMB for each SAE including information about the event and its outcome, dosing history of 
all study drugs, concomitant medications, the subject’s medical history and current conditions, 
and all relevant laboratory data.  Notification by e-mail, and FAX transmittal of all related study 
forms shall be made to the DSMB within 2 days of the occurrence of any SAE.  Information will 
be reviewed and a determination made of whether there was any possible relevance to the study 
drug.  Additional reporting to local IRBs will be done within 24 hours of the SAE. 
 
4. Non-Serious Adverse Events – At periodic intervals (quarterly during the course of the study 
and then again at its completion), the DSMB will be provided with un-blinded summaries of the 
numbers and rates of adverse events by treatment group.  These reports will include types of 
events, severity, and treatment phase.  Data on individual non-serious adverse events is not 
expected to be needed for this review. 
 
5. Other Safety-Related Reports – At three-month intervals throughout the course of the study, 
the DSMB will also receive unblinded summary reports of treatment retention and reasons for 
dropout, by treatment arm and study phase.   
 
6. Study Stopping Rules – If at any time during the course of the study, the DSMB judges that 
risk to subjects outweighs the potential benefits, the DSMB shall have the discretion and 
responsibility to recommend that the study be terminated.  
 
 
X. References: 
 
American Psychiatric Association.1994. Diagnostic and Statistical Manual of Mental Disorders, 
4th ed.  Washington, DC: American Psychiatric Press. 
 
Beck AT, Rush AJ, Shaw BF, Emery G.  Cognitive therapy of depression. 1979, New York: 
Guilford Press. 
 
Beck AT, Ward Ch, Mendelson M, Mock J, & Erbaugh J.  An inventory for measuring 
depression.  Archives of General Psychiatry 1961;4:53-63.   
 
Beck JS. Cognitive therapy: basics and beyond. 1995, New York. Guilford Press. 
 
Berenbaum H, Oltmanns TF. 1992. Emotional experience and expression in schizophrenia and 
depression. J Abnorm Psychol 101:37-44.  
 
Buchwald AM. 1977. Depressive mood and estimates of reinforcement frequency. J Abnorm 
Psychol 86:443-6. 
 
Campbell DT & Fiske DW.  Convergent and discrimination validation by the multitrait-
multimethod matrix.  Psychol Bull 1959;56(2):81-105 
 
Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav; 
1983 24(4):385-96. 
 


Version Date: 11/30/09 
12
Cook IA, Leuchter AF, Uijdehaage SH, Osata S, Holschneider DH, Abrams M, Rosenberg-
Thompson S. Altered cerebral energy utilization in late life depression. J Affect Disord. 1998; 
49(2): 89-99.  
 
Cook IA, Leuchter AF, Witte E, Abrams M, Uijdehaage SH, Stubbeman W, Rosenberg-
Thompson S, Anderson-Hanley C, Dunkin JJ. Neurophysiologic predictors of treatment response 
to fluoxetine in major depression. Psychiatry Res. 1999; 85(3): 263-73.  
 
Cook IA, Leuchster AF. Prefrontal changes and treatment response prediction in depression. 
Semin Clin Neuropsychiatry. 2001; 6(2): 113-20.  
 
Deckersbach T, Gershuny BS, & Otto MW.  Cognitive behavioral therapy for depression:  
Applications and outcome.  Psychiatric Clinics of North America 2000;23:795-809. 
 
Elkin I, Shea MT, Watkins JT, Imber SD, Sotksy SM, Collins JF, Glass DR, Pilkonis PA, Leber 
WR, Docherty JP.  National Institute of Mental Health Treatment of Depression Collaborative 
Research Program.  General effectiveness of treatments.  Arch Gen Psychiatry 1990;47(7):682-8. 
 
Endicott J, Nee J, Harrison W, Blumenthal R. Quality of Life Enjoyment and Satisfaction 
Questionnaire: a new measure. Psychopharmacol Bull 1993; 29(2): 321-6.  
 
Evans MD, Hollon Sd, DeRubeis RJ, Piasecki JM, Grove WM, Garvey MJ, Tuason VB.  
Differential relapse following cognitive therapy and pharmacotherpay for derpession.  Arch Gen 
Psychiatry 1992;155:1443-1445. 
 
Farabaugh AH, Mischoulon D, Fava M, Green C, Guyker W, Alpert J.  The potential relationship 
between levels of perceived stress and subtypes of major depressive disorder (MDD). Acta 
Psychiatrica Scandinavica 2004;110:1-6. 
 
Fava GA & Kellner R.  Prodromal symptoms in affective disorders.  Am J Psychiatry 
1991;148(7):823-30 
 
First, M.B., Spitzer, R.L., Gibbon, M., Williams, J.B.W.  Structured Clinical Interview for DSM-
IV Axis I Disorders with Psychotic Screen (SCID-I/P w/ Psychotic Screen) Version 2.0.  1995, 
New York, NY: Biometrics Research Department. 
 
Folstein MF, Folstein SE, McHugh PR: “Mini-mental state.” A practical method for grading the 
cognitive state of patients for the clinician. J Psychiatr Res 1975;12:189–198. 
 
Goldapple K, Segal Z, Garson C, Lau M, Bieling P, Kennedy S, Mayberg H. Modulation of 
cortical-limbic pathways in major depression: treatment specific effects of cognitive behavior 
therapy. Arch Gen Psychiatry. 2004; 61(1): 34-41.  
 
Gotlib IH. 1982. Self-reinforcement and depression in interpersonal interaction: the role of 
performance level. J Abnorm Psychol 91:3-13. 
 
Guy W (ed):  ECDEU Assessment Manual for Psychopharmacology, revised.  DHEW Pub. No. 
(ADM)76-338.  National Institute of Mental Health, Rockville, MD, 1976. 
 
Hamilton M: A rating scale for depression. J Neurol, Neurosurg and Psychiatry 1960;23:56-62. 
 


Version Date: 11/30/09 
13
Henriques, J.B. & Davidson, R.J. (2000). Decreased responsiveness to reward in depression. 
Cognition  and Emotion 14: 711-724. 
 
Hughes JR, Pleasants CN, Pickens RW. 1985. Measurement of reinforcement in depression: a 
pilot study. J Behav Ther Exp Psychiatry 16:231-6. 
 
Iosifescu DV, Greenwald S, Devlin P, Alpert JE, Hamill S, Fava M. Frontal EEG at 1 Week 
Predicts Clinical Response to SSRI Treatment in Major Depressive Disorder. American 
Psychiatric Association 159th Annual Meeting, Toronto, Canada, 2006 
 
Jarrett RB, Schaffer M, McIntire D, Witt-Browder A, Kraft D, Risser R.  Treatment of atypical 
depression with cognitive therapy or phenelzine.  Arch Gen Psychiatry 1999;56(5):431-437. 
 
Jay TM, Rocher C, Hotte M, Naudon L, Gurden H, Spedding M.  Plasticity at hippocampal to 
prefrontal cortex synapses is impaired by loss of dopamine and stress:  importance for psychiatric 
diseases.  Neurotox Res. 2004;6(3):233-244. 
 
Kellner R.  A symptom Questionnaire.  J Clin Psychiatry 1987;48;268-274. 
 
Kendall, P.C., Howard, B.L., & Hays, R.C. Self-referent speech and psychopathology:  The 
balance of positive and negative thinking.  Cog Ther Res 1989;13:583-598. 
 
Kessler LG, Burns BJ, Shapiro S, Tischler GI, Georger LK, Hough RL, Bodison D, Miller RH.  
Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States.  
Results from the National Comorbidity Study.  Arch Gen Psychaitry 1994;51:8-19. 
 
Knott VJ, Telner JI, Lapierre YD, Browne M, Horn ER. Quantitative EEG in the 
prediction of antidepressant response to imipramine. J Affect Disord. 1996; 39(3):175-84. 
 
 
Loas G. 1996. Vulnerability to depression: a model centered on anhedonia. J Affect Disord 
41:39-53. 
 
Levine J, Schooler NR.  SAFTEE:  A technique for the systematic assessment of side effects in 
clinical trials.  Psychopharmacol Bull. 1986;22(2):343-81.  
 
Marin RS, Biedrzycki RC, Finnciogullari S.  Reliability and validity of the Apathy Evaluation 
Scale.  Psychiatry Res 1991; 38:143-162. 
 
Meehl PE. 1975. Hedonic capacity: some conjectures. Bulletin of the Menninger Clinic 39:295-
307. 
 
McHorney CA, Ware JE, Raczek AE.  The MOS 36-item short-form health survey (SF-36):II.  
Psychometric and clinical tests of validity and measuring physical and mental health constructs.  
Med Care 1993;31(3):247-263. 
 
Muller MJ, Dragicevic A. Standardized rater training for the Hamilton Depression Rating Scale 
(HAMD-17) in psychiatric novices. J Aff Disord 2003;77(1):65-69. 
 


Version Date: 11/30/09 
14
Murr C, Widner B, Sperner-Unterweger B, Ledochowski M, Schubert C & Fuchs D.  Immune 
reactions links disease progression in cancer patients with depression.  Medical hypotheses 
2000;55:137-140. 
 
Murray CJ & Lopez AD.  Evidence-based health policy-lessons from the global burden of disease 
study.  Science 1996;274:740-743. 
 
Mynors-Wallis LM, Gath DH, Day A, baker F.  Randomized controlled trial of problem solving 
treatment, antidepressant medication, and combined treatment for major depression in primary 
care.  BMJ 2000;320(7226):26-30 
 
Nierenberg AA, Alpert JE, Pava J, Rosenbaum JF, Fava M.  Course and treatment of atypical 
depression.  J Clin Psychiatry 1998;56(18):5-9. 
 
Otto MV, Pava J & Sprich-Buckminster S.  Treatment of major depression:  application and 
efficacy of cognitive bheavioral therapy.  In Pollack MH, Otto MW, & Rosenbaum JF.  (Eds) 
Challenges in clinical practice:  Pharmacologic and psychosocial strategies.  1996,  New York: 
Guildford Press.   
 
Otto MW, Fava M, Penzava S. Bless E, Muller RT Rosenbaum JF.  Life event and cognitive 
predictors of perceived stress before and after treatment for major depression.  Cog Ther Res 
1997;21:409-420. 
 
Paykel E, Scott J, Cornwall P, Abbott R, Crane C, Pope M, Johnson A.  Duration of relapse 
prevention after cognitive therapy in residual depression:  follow up of controlled trial.  
Psychological Medicine 2005;35:59-68. 
 
Paykel ES, Scott J, Teasdale JD, Johnson AL, Garland A, Moore JR, Jenaway A, Cornwall PL, 
Hayhurst H, Abbot R, & Pope M.  Prevention of relapse in residual depression by cognitive 
therapy:  A controlled trial.  Arch Gen Psychiatry 1999;56:829-835. 
 
Rounsaville BJ, Carroll KM.  A stage model of behavioral therapies research:  getting started and 
moving on from stage 1.  Clin Psychology: Science and Practice 2001;12:133-142 
 
Rush AJ, Trivedi MH, Ibrahim HM, Carmody TJ, Arnow B, Klein DN, Markowitz JC, Ninan PT, 
Kornstein S, Manber R, Thase ME, Kocsis JH, Keller MB. The 16-Item Quick Inventory of 
Depressive Symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a 
psychometric evaluation in patients with chronic major depression. Biol Psychiatry 2003;54(5): 
573-83. 
 
Rush AJ, Trivedi MH, Wisniewski SR, Nierenberg AA, Steward JW, Warden D, Niederehe G, 
Thase ME, Lavori PW, Lebowitz BD, McGrath PJ, Rosenbaum JF, Sackheim HA, Kupfer DJ, 
Lutter J, Fava M. Acute and longer-term outcomes in depressed outpatients requiring one or 
several treatment steps: a STAR*D report. Am J Psychiatry 2006; 163: 1905-1917.  
 
Scott C, Tacchi MJ, Jones R, Scott J.  Acute and one-year outcome of a randomized controlled 
trial of brief cognitive therapy for major depressive disorder in primary care.  Br J Psychiatry 
1997;171:131-4. 
 
Simon GE, VonKorff M, Barlow W.  Health care costs of primary care patients with recognized 
depression.  Arch Gen Psychaitry 1998;52:850-856. 


Version Date: 11/30/09 
15
 
Simons AD, Murphy GE, Levine JL, & Wetzel RD.  Cognitive therapy and pharmacotherapy for 
depression:  sustained improvement over one year.  Arch Gen Psychiatry 1986;43:43-48 
 
Sloan DM, Strauss ME, Wisner KL. 2001. Diminished response to pleasant stimuli by depressed 
women. J Abnorm Psychol 110:488-93. 
 
Snaith RP, Hamilton M, Morley S, Humayan A, Hargreaves D, Trigwell P. A scale for the 
assessment of hedonic tone: the Snaith-Hamilton Pleasure Scale. British Journal of Psychiatry 
1995; 167: 99-103.  
 
Suslow T, Junghanns K, Arolt V. 2001. Detection of facial expressions of emotions in depression. 
Perceptual & Motor Skills 92:857-68.  
 
Tanaka-Matsumi J, Seiden DY & Lam KN.  The culturally informed functional assessment 
interview (CIFA):  A strategy for cross-cultural behavioral practice (1996).  Cog Behav Practice, 
3, 215-233. 
 
Teasdale JD, Segal ZV, Williams JM, Ridgeway VA, Soulsby JM, & Lau MA.  Prevention of 
relapse/recurrence in major depression by mindfulness-based cognitive therapy.  J Consult Clin 
Psychology 2000;68:615-23.  
 
Ulrich G, Haug HJ, Fahndrich E. Acute vs chronic EEG effects in maprotiline- and in 
clomipramine- treated depressive inpatients and the prediction of therapeutic outcome. J. Affect 
Disord. 1994; 32(3): 213-7.  
 
 
Ware JE.  Measuring patients’ views:  the optimum outcome measure.  BMJ 
1993;306(6890):1429-30. 
 
Weissman, A.N., & Beck, A.J. Development and validation of the dysfunctional attitude scale:  A 
preliminary investigator.  Paper presented at the annual meeting of the American Educational 
Research Association,1978, Toronto, Ontario, Canada. 
 
Wells KB, Stewart A, Hayes RD, Burnam MA, Rogers W, Daniels M, berry S, Greenfield S, 
Ware J.  The functioning and well-being of depressed patients:  Results from the Medical 
Outcomes Study.  J Amer Medical Assoc 1989;262:914-919. 
 
World Health Organization International classification of impairments, disabilities and handicaps.  
A manual of classification relating to the consequences of disease 1980. Geneva: World health 
Organization 
 
Young J., & Beck A.T. The development of the Cognitive Therapy Scale. Unpublished 
manuscript, Center for Cognitive Therapy, 1980, Philadelphia, PA. 
 
 
 

SAP_001.pdf: 
 
Quantitative Electroencephalogram (QEEG) Predictors of Response to Psychotherapy 
Versus Antidepressant Treatment in Depression 
 
Identifiers: NCT00824044 
 
Unique Protocol ID: 2008-P-000838 
 
Date prepared: 12/7/2017 
 
 
 


Summary: 
 
A-priori expectation from prior medication work is a decrease in theta activity from 
baseline at week 1 in responders, and an increase in non-responders. Prior work showed 
an increase in theta activity in placebo responders (and little change in non-responders). 
(See attached manuscript that describes changes in theta Cordance over time in both 
placebo and medication groups. See Fig 4 on page 128.) Also, ATR is expected to be 
higher in medication responders than non-responders after 1 week on antidepressant. 
 
Methods: 
 
ATR and Power metrics were calculated from available EEGs at baseline and subsequent 
visits. Change in Power between baseline and subsequent visits were derived. 
The data were analyzed for 3 groups: Pooled, Drug-only, Drug+CBT. 
 
In a completer analysis (not an intent to treat), independent sample t tests were used to 
assess the relationship between responders (greater than or equal to 50 % reduction in 
the primary outcome (HAMD-17 score)) and change in QEEG metrics across time.