A Randomized Controlled Trial of N-Acetylcysteine for Alcohol Use Disorder and Comorbid Post Traumatic Stress Disorder
Phase: PHASE2
Conditions: Post Traumatic Stress Disorder (PTSD), Addiction, Alcohol Abuse
Sponsor: Medical University of South Carolina

 
PROTOCOL TITLE: A Randomized Controlled Trial of N-Acetylcysteine for Alcohol Use Disorder and 
Comorbid PTSD 
NCT02966873 
November 9, 2019 

 
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PROTOCOL TITLE: A Randomized Controlled Trial of N-Acetylcysteine for Alcohol Use Disorder and 
Comorbid PTSD 
 
The primary objective of the proposed Phase II study is to evaluate the effects of NAC in reducing (1) AUD 
severity and (2) PTSD symptomatology among individuals (N=200) with current AUD and PTSD. We will 
also use functional magnetic resonance imaging (fMRI) and proton magnetic resonance spectroscopy 
(MRS) to investigate the neural circuitry and neurochemistry underlying comorbid AUD/PTSD and 
prognostic indicators of positive treatment response. Secondary objectives are to evaluate the effects of 
NAC on impairment in associated areas of functioning (e.g., depression, anxiety, sleep, risky behaviors). 
In order to accomplish this we will (1) employ an intent-to-treat, double-blind, placebo-controlled 
randomized controlled trial that will consist of 12 weeks of treatment with NAC (2400 mg per day) or 
placebo medication; (2) examine standardized, repeated dependent measures of clinical outcomes at 
baseline, week 6, week 12, and 3-, 6-, and 12-month follow-up; and (3) employ advanced neuroimaging 
methodologies, a laboratory cue paradigm, and collect biologic measures of alcohol consumption. The 
following specific aims are proposed: 
 
Specific Aim 1:  To determine the efficacy of N-acetylcysteine (NAC), as compared to placebo, in 
reducing alcohol use severity. 
 
Specific Aim 2:  To determine the efficacy of N-acetylcysteine (NAC), as compared to placebo, in 
reducing PTSD symptomatology. 
 
Specific Aim 3: To use multimodal neuroimaging techniques to investigate the pathophysiology 
underlying AUD and comorbid PTSD, and prognostic indicators of treatment outcome. 
 
 
Human Subjects Involvement and Characteristics: A total of 200 adults between the ages of 18 and 
70 will be recruited over a 5-year period. The inclusion/exclusion criteria are as follows: 
 
Inclusion Criteria:  
1. Male or female; any race or ethnicity; age 18 to 70 years old.  
2. Subjects must be able to comprehend English.  
3. Meet DSM-5 criteria for current alcohol use disorder (AUD). 
4. Meet DSM-5 criteria for current PTSD or subthreshold PTSD. Subjects may also meet criteria for 
a mood disorder (except bipolar affective disorder, see Exclusion Criteria) or other anxiety disorders 
(panic disorder, agoraphobia, social phobia, generalized anxiety disorder, or obsessive compulsive 
disorder). The inclusion of subjects with affective and other anxiety disorders is essential because 
of the marked frequency of the co-existence of mood and other anxiety disorders among patients 
with AUD and PTSD (Brady et al., 2000; Kessler et al., 2005). Subjects may meet DSM-5 criteria 
for another substance use disorder as long as AUD is the primary substance of choice.  
5. Subjects taking psychotropic medications will be required to be maintained on a stable dose for at 
least four weeks before treatment initiation. This is because initiation or change of medications 
during the course of the trial may interfere with interpretation of results.  
6. Must consent to random assignment to N-acetylcysteine (NAC) or placebo. 
7. Must consent to complete all treatment and follow-up visits. 
8. Must live within 50 miles (one hour) of MUSC in Charleston, SC or be willing to travel to MUSC for 
visits. 
 
Exclusion Criteria: 
1. Subjects meeting DSM-5 criteria for a history of or current psychotic or bipolar affective disorders, 
as the study protocol may be therapeutically insufficient.  
2. Subjects with a current eating disorder (bulimia, anorexia nervosa) or with dissociative identity 
disorder, as they are likely to require specific time-intensive psychotherapy. 

 
Page 2 of 17 
 
3. Subjects experiencing significant withdrawal symptoms, as evidence by a score of 10 or above on 
the Clinical Institute Withdrawal Assessment of Alcohol (CIWA). These subjects will be referred for 
clinical detoxification and may be re-assessed for study eligibility after medically supervised 
detoxification has been completed.  
4. Individuals considered an immediate suicide risk or who are likely to require hospitalization during 
the course of the study for suicidality. 
5. Women who are pregnant, nursing or not practicing an effective form of birth control. 
6. Evidence of liver failure; alanine aminotransferase (ALT) or aspartate aminotransferase (AST) 
levels greater than 3 times the upper limit of normal; asthma or any clinically significant medical 
condition that in the opinion of the investigator would adversely affect safety or study participation. 
7. Use of carbamazepine, phenytoin, nitrous oxide, methotrexate, 6-azauridine triacetate, or 
nitroglycerin within the last 14 days or any other medication felt to have a hazardous interaction if 
taken with NAC. 
8. History of childhood or adult seizures of any cause. 
 
No special classes of subjects, such as, pregnant women, prisoners, institutional individuals, or others will 
be recruited for this study. Both male and female participants will be recruited. There will be no exclusion 
based on race or ethnicity. Participants will be recruited without preference for gender, race, ethnicity or 
socio-economic status.  
 
APPROACH 
General Procedures.  Interested individuals will be prescreened by telephone, in person, or online. 
Individuals who meet the basic inclusion/exclusion criteria will be invited to come into the office for a 
comprehensive baseline assessment.  Potential participants will be given a full description of the study and 
asked to read and sign an IRB-approved informed consent form before any study procedures or 
assessments are conducted. Baseline assessment will include diagnostic interviews, history and physical 
examination, assessment of concomitant medications, self-report questionnaires, breathalyzer and 
collection of a urine sample to screen for alcohol, drug and nicotine use as well as pregnancy (females 
only).  Ineligible participants will be referred clinically for treatment.  
Following the baseline visit, eligible participants will complete a randomization visit prior to 
medication initiation. Eligible participants may also complete a neuroimaging session (described in the 
Neuroimaging Component section) prior to medication initiation. Participants will enter a 12-week, 
randomized, double-blind, placebo-controlled trial in which they will be seen weekly for health/safety 
monitoring, dosing/medication dispensation, and completion of assessments. Eligible participants may 
also complete a second neuroimaging session at Week 8 (Post treatment). At 3-, 6-, and 12-month post-
treatment, follow-up visits will occur as specified below.  
Recruitment.  The primary recruitment site will be the outpatient Center for Drug and Alcohol 
Programs (CDAP) at MUSC. In addition, we will place IRB-approved study flyers and other approved study 
materials in prominent locations in MUSC and community-based clinics, and place advertisements on 
social networking sites (e.g., Craigslist, Facebook). Local area locations of residential hubs and community 
providers (e.g., restaurants, entertainment venues) will also be accessed with the appropriate permissions 
to have approved advertising available for public viewing. We will use the assistance of various recruitment 
and advertising materials such as coffee mugs, pens, etc.  We will also use direct mailing methods of 
recruitment (through Charleston Water Systems and other as necessary) as well as other multi-media 
methods available through Post and Courier, including Geotargeting, Extended Email Targeting and Site 
re-targeting. Participants from past MUSC research studies who have consented to be contacted for future 
research studies will be recruited via telephone screening and/or e-mails. These individuals will be referred 
to us via other MUSC researchers, or they may have indicated consent to be contacted about future 
research studies within their MUSC medical records.  
 
 
 

 
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Assessment Instruments.  After the informed consent procedure, subjects will complete a battery 
of assessments. The instruments to be used (Table 1) were selected because many are standardized, 
have good psychometric properties, are widely used and have been used by our research group. 
Combined use of ethanol metabolites and traditional biomarkers (i.e., carbohydrate deficient transferrin 
and ethyl glucuronide) will be utilized to monitor drinking and corroborate self-report measures108-111. 
 
 
Primary Outcome Measures. The primary outcome measures are described in this section. 
Information regarding additional measures can be found in Table 1. The primary clinical outcomes include:  
(1) alcohol use severity (TLFB for alcohol consumption), and (2) PTSD symptom severity (CAPS-5 for 
clinician-rated and PCL-5 for self-reported symptoms). The TLFB obtains retrospective self-report of 
alcohol use by using a calendar and memory prompts to stimulate recall112. Quantity and frequency of use 
are obtained (e.g., total number of standard drink units, percent of days drinking, heavy drinking days as 
defined as 5 or more drinks for a man or 4 or more drinks for a woman) as well as abstinence (yes/no). 
The TLFB will assess consumption of alcohol as well as nicotine and other substances for 60 days prior to 
study entry, weekly during treatment, and at follow-up. The TLFB yields consistently high test-retest 
reliability and convergent validity with other self-reports and collateral reports113. The OCDS is a 14-item 
self-report measure of craving with high internal consistency and concurrent validity and good predictive 
validity of future drinking114-115. The CAPS for DSM-5 is a 30-item structured diagnostic interview and gold 
standard for assessing PTSD116. The CAPS assesses trauma history and has excellent psychometric 
properties and diagnostic efficiency117. The PCL for DSM-5 is a 20-item self-report measure that assesses 
PTSD severity and has excellent psychometric characteristics116,118. 
 
STUDY INTERVENTIONS AND PROCEDURES 
 
Overview. Subjects will be randomized to NAC or placebo, and all subjects will receive cognitive-
behavioral therapy (CBT) targeting AUD (12, weekly 60-minute sessions). Note that subjects will not 
receive any other CBT or treatments during the study. All services received (e.g., self-help groups, case 
management) will be carefully monitored and tracked at weekly visits.  
 
Study Medication, Dosage, and Administration.  Participants will be randomly assigned to 
receive NAC (2400 mg/day) or placebo for 12 weeks. The dose and length of treatment are based on 
previous research92,105. The starting dose of NAC is 1200 mg twice daily (2400 mg/day). Equivalent 
numbers of identical appearing placebo capsules will be dispensed.  All NAC and placebo capsules will 
contain riboflavin 25 mg as a biomarker to assess medication compliance.  Participants who wish to take 
a multivitamin during the treatment phase of the study will be given a multivitamin (Tri-Vi-Sol) that does 
not contain riboflavin. Weekly pill counts and documentation of missed doses will be carefully recorded at 
each visit. Study medications (USP-grade NAC and matched placebo capsules) will be compounded by 
Pitt Street Pharmacy in Mount Pleasant, South Carolina. Treatment assignment will follow a pre-arranged 
randomization scheme and will be carried out by a pharmacist not involved in clinical management of 
participants (to preserve the double-blind design). Research staff will administer the study medication or 
placebo at the twice-weekly visits, and participants will be given take-home doses to self-administer on the 
days in between study visits.  Subjects will be maintained at the target dose for 12 weeks. Side effects and 
adverse events will be evaluated weekly. 
 
Study design overview.  Participants are randomized to receive 12, 60-minute sessions of CBT for alcohol use disorder 
and either NAC (2400 mg qd) or placebo. Weekly visits during the treatment phase (weeks 1-12). Follow-up visits at 3, 6 
and 12 months post-treatment.   

 
Page 4 of 17 
 
 
Cognitive Behavioral Therapy.  All subjects will receive 12, weekly 60-minute sessions of 
individual CBT based on NIAAAA’s Project MATCH manual119 (see Appendix for manual).  Examples of 
session topics include: assessing high-risk situations, coping with cravings and urges to drink, managing 
thoughts about drinking, drink refusal skills, seemingly irrelevant decisions, and enhancing social support 
networks.  Receipt of weekly CBT during the treatment phase will facilitate retention and medication 
adherence, and ensure that all participants receive adequate psychosocial support, regardless of 
medication arm.   
 
Therapist Training, Supervision and Fidelity Monitoring: All therapists will be licensed Masters or 
Doctoral level clinicians with experience delivering CBT.  During the lead-in phase of the study, therapists 
will receive extensive training in the CBT protocol. Study therapists will complete four phases of training: 
(1) didactic review of intervention-specific theory, (2) manual review, (3) a two-day training workshop, and 
(4) subsequent completion of two pilot study cases in which all sessions will be recorded and rated for 
adherence and competency. Throughout the study, therapists will receive weekly supervision focusing on 
manual adherence, and any clinical concerns about particular patients. If during the course of the study it 
is determined upon review of therapy sessions and supervisory sessions that a therapist is not competent 
or does not adhere sufficiently to the manual, the therapist will be replaced. To assure the therapy is 
delivered consistent with manual guidelines, all therapy sessions will be recorded and 25% of randomly 
selected sessions will be evaluated using methods developed in the NIMH Collaborative Study121.  
 
Telehealth. Participants in this research study may choose to do study visits and therapy sessions 
via home-based telehealth (HBT) care (i.e., service delivery to patients in their homes using consumer-
friendly, video-conferencing technology) which may likely enhance retention by directly circumventing 
financial and transportation barriers associated with traveling to MUSC for in-person sessions. HBT 
sessions will be delivered via standard desk, laptop computer, tablet, or smartphone running MUSC/VA 
approved applications. HBT participants will be asked to come to our office for the baseline visit, week 6 
visit and week 12 visit. At each visit for HBT participants, in lieu of a breathalyzer test, an alcohol saliva 
test will be used to measure blood alcohol concentration (BAC) on a weekly basis for telehealth visits. In 
front of the camera, participants will open the one-time use test strip, place the strip on tongue for 10 
seconds, then hold the test strip and a color chart to the camera for a study team member to assess. 
Samples reading >0.01 g/dl will be considered positive. 
 
Table 1.  Assessment Instruments and Timeline 
 
Instrument Name 
 
Purpose/Domain 
 
BSL 
Randomization 
 
Weekly 
 
EOT 
(Wk 
12) 
 
3, 6 & 12 
Months 
Post-
Treatment 
Informed Consent 
Obtain informed consent 
X 
 
 
 
 
Demographics Form 
Characterize sample 
X 
 
 
 
 
History of Head Injuries Assessment 
Assess head injuries, TBI, concussion 
X 
 
 
 
 
History and Physical Examination 
Assess medical problems & eligibility 
X 
 
 
 
 
Psychiatric Interview 
Assess DSM-5 psychiatric disorders 
X 
 
 
 
 
Columbia-Suicide Severity Rating Scale (CSSR) 
Assess for suicidality  
X 
 
X  
(Wk 6) 
X 
 
Pregnancy Test for Female Subjects+ 
To assess for pregnancy 
X 
X 
X  
(Wk 6,) 
 
 
Clinical Institute Withdrawal Assessment of 
Alcohol-Revised: CIWA-Ar 
Study eligibility, safety 
X 
 
 
 
 
Urine Drug Screen tests: UDS 
Study eligibility, assess drug use 
X 
 
X 
(Wk 6) 
X 
X 
Adverse Events and Vital Signs 
Monitor AEs and safety  
X 
X 
X 
X 
X 
Concurrent Medications Form 
Monitor medications 
X 
X 
X 
X 
X 
Medication Adherence Log 
Assess medication compliance 
 
X 
X 
X 
 
Life Events Checklist: LEC 
Assess trauma exposure 
X 
 
 
 
 
Childhood Trauma Questionnaire: CTQ 
Assess childhood trauma 
X 
 
 
 
 

 
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Clinician Administered PTSD Scale: CAPS-5^ 
PTSD symptom severity (clinician 
rated) 
X 
 
X 
(Wk 6) 
X 
X 
PTSD Checklist: PCL-5 
PTSD symptoms  severity  (self-
report) 
X 
 
X 
X 
X 
Time Line Follow-Back: TLFB 
Alcohol use (amount and frequency) 
X 
X 
X 
X 
X 
Obsessive Compulsive Drinking Scale: OCDS 
Craving  
X 
X 
X 
X 
X 
Visual Analog Craving Scale (VAS) 
Measure of alcohol craving, mood, 
stress, amount, frequency 
X 
X 
X 
X 
X 
Trauma Symptoms of Discrimination Scale 
Assess discrimination 
X 
 
 
 
 
Everyday Discrimination Scale 
Assess discrimination 
X 
 
 
 
 
Race Based Trauma and Stressor Checklist 
Assess race based trauma 
X 
 
X 
(Wk 6) 
X 
X 
Fagerstrom Survey 
Assess nicotine use 
X 
 
 
X 
X 
Satisfaction Questionnaire  
Asses overall satisfaction of services  
 
 
 
X 
X 
(Mo. 12) 
Breathalyzer test 
Assess alcohol use (biological) 
X 
X 
X 
X 
X 
Alcohol Saliva Test (HBT care only) 
Assess alcohol use (biological) 
X 
X 
X 
X 
X 
Alcohol Use Disorders Identification Test: AUDIT 
Assess drinking behavior & problems 
X 
 
 
 
X 
(Mo. 12) 
Urine riboflavin test 
Biomarker of medication compliance 
 
X 
X  
(Wk 6) 
X 
 
Ethylglucuronide (EtG)  
Biomarker of alcohol use  
 
X 
X 
(Wk 6) 
X 
X 
 
Beck Depression Inventory-II: BDI-lI 
Measure depression 
X 
 
X 
X 
X 
Penetration of the Blind 
Assess condition prediction 
 
 
 
X 
 
Treatment Services Review 
Monitor services utilization 
X 
X 
X 
X 
X 
Cognitive Functioning Tests 
Measure cognitive functioning  
 
X 
X 
(Wk 6) 
X 
 
Cognitive Behavioral Therapy: CBT 
Weekly CBT for alcohol use 
 
 
♦ 
♦ 
 
Note. BSL = Baseline. EOT = End-of-Treatment. +at baseline, before med initiation, before imaging sessions, and at Wk 6. 
 
 
Neuroimaging Component.  Given the severe negative outcomes associated with co-occurring 
AUD and PTSD and lack of effective treatments, investigation of the neural circuitry and neurochemistry 
underlying AUD/PTSD and involved in positive therapeutic response to NAC could be important in guiding 
future studies targeting this circuitry using other therapeutic agents or treatment modalities. To that end, 
fMRI and 1H-MRS data will be acquired at baseline and end of treatment on all fMRI-eligible participants 
to examine (a) glutamate concentrations, (b) resting state connectivity, and (c) response to alcohol, trauma, 
and 
neutral 
cues. 
Scans 
will 
be 
conducted at the MUSC Center for 
Biomedical Imaging (see Facilities and 
Resources section), which houses a 
Siemens 3T TIM Trio MRI scanner 
(Siemens 
Medical, 
Germany). 
The 
neuroimaging component will involve 
three visits, each lasting 60-90 minutes. 
During visit 1 (baseline screening visit), 
we 
will 
develop 
the 
personalized 
imagery scripts for trauma and/or racial-based trauma, alcohol and neutral cues. During visit 2 
(Randomization Visit, prior to medication initiation) and visit 3 (Post Treatment – 8 Visit), we will conduct 
the fMRI and 1H-MRS scanning procedures. Women of childbearing age will receive a urine pregnancy 
test before each MRI. Imagery scripts will be developed according to standardized procedures41 and 
employed in our ongoing research (see Preliminary Studies section). The Wk 8 scan will be conducted 
approximately one hour post-NAC administration to ensure findings reflect effects of NAC122-123. 
Participants will be screened for metal using a handheld metal detector. Trained staff will position subjects 
on the scanner bed with foam padding placed around their head to prevent motion. Participants will wear 
headphones to listen to the audio-recorded scripts. During initial scanner tuning, localizing, and structural 
Overview of neuroimaging session.  Assessment of (a) ACC glutamate, (b) resting 
connectivity, and (c) response to alcohol, trauma and neutral cues. 
Neutral 1 
Alcohol 1 
5 min 
6 min 
6 min 
6 min 
Resting State 
Structural 
Neutral 1 
Trauma 1 
1H-MRS 
15 min 

 
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scanning, participants will be shown “relaxing” images (i.e., 20 scenic pictures, each displayed for 30 sec). 
For co-registration and normalization of functional images, a high resolution T1-weighted MPRAGE 
anatomical image will be acquired with the following parameters: TR = 2100 ms, TE = 4.18 ms, flip angle 
= 12°, field of view = 256 mm, slice thickness 1.0 mm. The scanning planes will be oriented parallel to the 
anterior commissure–posterior commissure line. The ACC voxel for 1H-MRS will be placed on midsagittal 
T1-weighted images, anterior to the genu of the corpus callosum, with the ventral edge of the voxel aligned 
with the dorsal edge of the genu and a voxel size of 3 x 2.5 x 2.5 cm3 will be selected. Following auto-
shimming, single-voxel water-suppressed 1H-MRS spectra will be acquired using a Point Resolved 
Spectroscopy (PRESS) sequence: Repetition Time (TR) = 2000ms; Echo Time (TE) = 40ms; number of 
averages = 128); an unsuppressed water spectrum will be co-acquired (TE = 40ms, number of averages 
= 16), scaled for partial volume effects and relaxation, and used as a concentration reference123.  
Next, participants will be asked to relax and keep their eyes opened and fixed on a cross-hair for 
six-minutes while resting state data are collected. Following this, participants will be exposed to alcohol, 
trauma and neutral cues. We will use a block design consisting of two 6-minute runs. During the alcohol 
cue run, participants will hear an audio recording describing in detail the last time they consumed alcohol. 
The alcohol cue run will be divided into two, 3-minute blocks of alcohol and neutral cues. During the trauma 
cue run, participants will hear an audio recording describing in detail their traumatic event.  The trauma 
cue run will be also divided into two, 3-minute blocks of trauma and neutral cues. During the neutral cue 
run, participants will hear an audio recording describing something neutral, such as their typical morning 
routine. To minimize potential carry-over effects, the runs will be counterbalanced so that half of the 
subjects in the placebo group and half of the subjects in the NAC group are exposed to the alcohol cue 
first and the remaining participants in each group are exposed to the trauma cue first. T2*-weighted 
gradient-echo planar images (EPI) will be acquired with the following parameters: TR = 2000 ms, TE = 27 
ms, flip angle = 76º, matrix 64 x 64, field of view = 23 cm, slice thickness = 3.7 mm with no gap, with 36 
slices to cover the entire brain. 
Subject Compensation. Subjects will receive $50 for the baseline visit (plus $25 bonus for showing up 
on schedule), $25 for the medication initiation (randomization) visit, $40 for completing each of the 
subsequent weekly visits during the treatment phase, and $100 for the 3-month, $100 for the 6-month, and 
$100 for the 12-month post-treatment follow-up visits. Patients will also receive a $25 bonus after 
completing the WK 6 and a $60 bonus for completing WK 12 visit. Participants may also be eligible to 
participate in the optional MRI portion of the study. The participant may receive $25 for script development, 
$75 for the first neuroimaging visit and $125 for the second neuroimaging visit. Compensation is available 
in the form of cash or check. Thus, the total amount subjects may receive for study participation is $1190 
 
STATISTICAL ANALYSES 
General. Baseline clinical and demographic characteristics will be collected and contrasts 
performed between treatment groups. Continuous and ordinal characteristics will be compared using a 
Wilcoxon Rank-Sum test statistic. Categorical characteristics will be compared using a Pearson Chi-
Square test statistic.  Baseline characteristics found to be significantly associated with primary outcome 
measures will be included as covariates in the analyses. All analyses will be performed on the intent-to-
treat sample consisting of all randomized subjects. All analysis will be conducted using SAS v9.3 (SAS 
Institute, Cary, NC, USA).   
Randomization. We will stratify by (1) AUD severity (TLFB) and (2) PTSD severity (CAP-5). 
Stratified random block randomization will be used to balance the randomization assignment with respect 
to these strata. The purpose of stratification is to distribute these potential prognostic factors equally across 
treatment groups.  
 
 
Power and Sample Size. This study is powered to estimate the efficacy of NAC on the reduction 
in PTSD symptoms, alcohol use, and craving at the end of the treatment phase (final 3 weeks of treatment). 
This study builds on a recently completed pilot trial of NAC that assessed efficacy in reduction of PTSD 
symptomology and craving in a cohort of Veterans with PTSD and substance use disorders. In this 
promising pilot trial, we observed a clinically significant decrease in CAPS total scores following 8 weeks 
of treatment with NAC (Δ=26.8) while the placebo response was attenuated (Δ=17.1). Assuming a similar 
difference in treatment effects in a larger population, we will have 80% power with a type 1 error rate of 
5% to detect this difference with 75 participants randomized to each of the two treatment arms (total 

 
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N=150).  In the same pilot trial, 77% of subjects completed the 8 week trial; we anticipate similar retention 
in the proposed study. Thus, a randomized sample size of 100 participants per treatment arm (total N=200) 
would maintain power to detect the clinically significant difference stated above in the presence of ~25% 
attrition during the study.  Similarly, in the same pilot study, reductions in craving were significantly greater 
in the NAC vs. placebo treated group (Δ=3.0 vs. Δ=1.3). The randomized sample size of 100 per treatment 
arm provides >95% power with a type 1 error rate of 5% to detect this effect in the presence of 25% attrition 
during the study period.  It is hypothesized that NAC reduces the risk of drinking as compared to placebo. 
With this stated sample size, a 25% decreased risk in the consumption of standard drinks can be detected 
with at least 80% power between the NAC and placebo treated arms (Risk Ratio=0.75).  
 
Power calculations for the neuroimaging component are based on previous studies of individuals 
with PTSD or substance use disorders.  Based on our previous work and the extant literature, we 
conservatively estimate ~70% of participants (140/200) will be eligible and volunteer for the neuroimaging 
component. A recent study of 29 individuals (PTSD=14, controls=15) observed significant differences in 
PFC-AMY resting state connectivity50. The effect size for this difference was 0.8 (Cohen’s d). Thus, power 
(1-ß) reaches .80 with a total sample size of N=26 (two-tailed, alpha=.05). Another study of 45 cocaine-
dependent individuals found that individuals who relapsed (n=24) had significantly lower PFC-AMY 
connectivity at baseline as compared to individuals who did not relapse (n=21)49. The effect size for this 
reduction was 1.0. Thus, power (1-ß) reaches .80 with N=34 subjects (two-tailed, alpha=.05).  For 
exploratory hypothesis 4, the only published study of NAC effects on brain glutamate found that one dose 
of NAC vs. placebo administered 1 hour prior to scanning, led to a significant modification in ACC glutamate 
with an effect size of d = 2.6 in individuals with cocaine dependence. Although the present study will be 
examining the effects of chronic NAC treatment in individuals with AUD and PTSD, an effect of similar 
magnitude would provide power > 0.99. The present study is therefore well-powered to test the proposed 
hypotheses. 
 
Hypotheses. The hypotheses and statistical approaches for testing each hypothesis are listed below.  
 
Hypothesis 1: NAC treatment will result in significantly greater reduction in AUD severity and craving, as 
compared to placebo.  To test this hypothesis, a mixed effects modeling framework will be specified with 
the total number of standard drinks consumed and the OCDS total score as the primary outcomes. A 
Poisson distribution will be assumed with a logarithm link function to assess the effects of NAC treatment 
and the baseline number of total standard drinks (past 60 days) prior to treatment entry. Over-dispersion 
due to a wider than expected distribution in discrete count models (from heterogeneity) can have a 
significant impact on parameter inference, thus when detected, a negative binomial (NB) distribution will 
be specified. Further, data collected in substance abuse studies often contain a preponderance of zeros, 
and these zeros in the distribution can be considered sampling zeros in the Poisson process125. If this 
excess is present in the distribution, two-part Hurdle models (Poisson and NB) will be explored126-127. The 
hurdle part of the model will allow us to estimate the probability of abstinence during the study period (as 
an odds ratio) while the Poisson/NB portion will allow for the assessment of the risk of an increase in using 
days beyond zero during the study period (as a risk ratio). Model fit of the Poisson (Hurdle Poisson) model 
will be compared to the Negative Binomial (Hurdle NB) model by practical examination of the deviance 
and Pearson chi-square values (Likelihood Ratio) with respect to their degrees of freedom.  Baseline 
alcohol use and craving will be included in pertinent models as covariates. Additional alcohol use outcomes 
will be assessed: percentage of drinking days (DD) and the number of heavy drinking day (HDD). End of 
study abstinence will be assessed using logistic regression models. DD and HDD will be assessed using 
count models. 
 
Hypothesis 2: NAC treatment will result in significantly greater reduction in PTSD severity as compared to 
placebo. To test this hypothesis, generalized linear mixed effects regression models with a Gaussian 
distribution will be developed to assess group differences while adjusting for baseline CAPS-5 scores.  
Assumptions of residual normality and homoscedasticity will be checked using statistical test and graphical 
methods (Residual and Q-Q plots, and Shapiro-Wilk tests) and transformation will be done as necessary. 
Restricted maximum likelihood (REML) methods will be used to estimate the fixed effects and variance 
components. 
 

 
Page 8 of 17 
 
Hypothesis 3A: Prefrontal cortex-amygdala connectivity at rest and in response to alcohol vs. neutral cues 
will predict reduction in alcohol use severity (total standard drinks). 
Hypothesis 3B: Prefrontal cortex-amygdala connectivity at rest and in response to trauma vs. neutral cues 
will predict reduction in PTSD severity (CAPS-5). 
For hypotheses 3A and 3B, the following preprocessing and analytical parameters will be used. 
Preprocessing: Post-acquisition preprocessing and statistical analysis of imaging data will be performed 
using FEAT (FMRI Expert Analysis Tool) Version 5.98, part of FSL (FMRIB's Software Library). Data will 
be preprocessed using scripting tools from FEAT. Non-brain signal will be removed using FSL’s BET brain 
extraction. Scans will be corrected for motion using FSL’s linear registration and scans will be spatially 
smoothed using a Gaussian kernel of 8 mm FWHM. Participants with head motion > 0.2 mm will be 
excluded from the analyses127. Motion artifacts will be identified and scrubbed from each subject’s data. 
Scans will be spatially co-registered with a standardized anatomical template (Montreal Neurological 
Institute) using a 12 parameter affine transformation. Analysis: Functional connectivity will be measured 
using a psychophysiologic interaction seed-based approach129-130. PPI analysis is used to assess the 
functional coupling between different brain regions. The PPI is defined as the change in connectivity of 
one area (i.e., the seed region) to another in relation to the experimental context (e.g., alcohol vs. neutral 
cues). PPI analyses for resting state, trauma and alcohol cues will be performed separately. Customized 
sq. wave forms representing the trauma run (1=trauma block and -1=neutral block) and the alcohol cue 
run (1=alcohol block and -1=neutral block) the duration of each block will be convolved with a double-
gamma hemodynamic response function. A mask of the seed region will be made using a 12-mm diameter 
sphere located in the center of the AMY using the MNI coordinates (x, y, z = ±22, 0, -22). The 
transformation parameters described above will also be applied to the mask. For each subject, the mean 
corrected and high pass filtered time series of the BOLD signal in the AMY will be extracted and used in a 
single subject whole brain PPI analysis. The PPI model will include the task vector, time series of the BOLD 
signal in the AMY, a term representing the positive task x seed interaction, and a term representing the 
negative task x seed interaction. The first level analysis will generate contrast images of the parameter 
estimates for each of the four regressors. Voxels will be thresholded at Z>2.3 using a corrected cluster 
threshold of p=0.05. The contrast images of the parameter estimates of the positive and negative task x 
seed interactions will be combined for group-level t-tests to identify regions that exhibited altered 
connectivity with the AMY during the trauma and alcohol cues as compared to neutral cues. All group-level 
results will be thresholded at Z>2.3 using a corrected cluster threshold of p=0.05. Separate linear 
regression tests will be used to test for associations between PFC-AMY connectivity at baseline and 
improvement in AUD and PTSD symptoms during treatment. Changes in total standard drinks will be 
regressed against the parameter estimate obtained from the center voxel from each cluster that exhibited 
a significant task x seed interaction with the AMY at rest and in response to the alcohol cues (Hypothesis 
3A). Changes in CAPS-5 total scores will be regressed against the parameter estimate obtained from the 
center voxel from each cluster that exhibited a significant task x seed interaction with the AMY at rest and 
in response to the trauma cue (Hypothesis 3B). Further exploratory analyses will examine change in PFC-
AMY connectivity from pre- to post-treatment and associations with alcohol and PTSD outcomes, both 
within and between medication groups. 
Exploratory Hypothesis 4: NAC treatment will be associated with a significant change in ACC glutamate 
concentrations, as compared to placebo. Change in ACC glutamate concentrations will be associated with 
alcohol and PTSD outcomes.  
 
Analysis of PRESS data will be conducted using LC Model 6.3131, an operator-independent curve-
fitting software package that uses least-squares estimation for quantifying metabolite concentrations; the 
basis set for TE = 40ms is provided by the vendor and includes a number of metabolites including 
glutamate, creatine, glutamine, N-acetylaspartate, and phosphocholine. Only metabolites with fitting 
uncertainties (Cramer-Rao Lower Bound values) < 20% of SD in the LC Model output will be retained for 
analysis. LC Model includes standardized zero filling, Fourier transformation, and automated phase, 
baseline and eddy current correction. To address variability in within-voxel tissue composition we will 
extract and segment T1-weighted images into partial volume maps of gray matter (GM), white matter (WM), 
and CSF using FSL tools, match the coordinates and size of the 1H-MRS voxel with the segmented images 
and extract the tissue fractions within the voxel, correct the raw values obtained from the LC model (scaled 

 
Page 9 of 17 
 
to water) for CSF and coil loading, and calculate each participant’s GM to brain matter (GM/[GM+WM]) 
ratio for use as a covariate in the analyses. Change in glutamate levels will be analyzed as a mixed model 
(SAS PROC MIXED) with time as a within subject variable and medication group as between subject 
variables. Variables known to influence ACC glutamate levels (e.g., age, smoking status, tissue 
composition) will be considered as covariates. Further exploratory regression analyses will examine 
associations between glutamate difference scores and clinical outcomes (e.g., change in total standard 
drinks and CAPS-5 score) both within and between medication groups. 
 
Summary: The proposed study will answer important questions regarding the potential of NAC as an 
effective pharmacotherapy for AUD and comorbid PTSD, and elucidate possible mechanisms underlying 
improved outcomes. The findings from this study have the potential to significantly improve the standard 
of patient care and accelerate research on the treatment of AUD and co-occurring PTSD 
 
 
 
 

 
Page 10 of 17 
 
REFERENCES 
 
1. 
Hasin, D.S., Stinson, F.S., Ogburn, E., & Grant, B.F. (2007). Prevalence, correlates, disability, and 
comorbidity of DSM-IV alcohol abuse and dependence in the United States: results from the National 
Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry, 64(7):830-842. 
2. 
Lozano, R., Naghavi, M., Foreman, K., et al. (2010). Global and regional mortality from 235 causes of 
death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease 
Study. Lancet, 380(9859):2095-2128. 
3. 
Rehm, J. (2011). The risks associated with alcohol use and alcoholism. Alcohol Res Health, 34(2), 
135-143. 
4. 
Grant, B. F., Goldstein, R.B., Saha, T.D., et al. (2015). Epidemiology of DSM-5 alcohol use disorder: 
Results from the National Epidemiologic Survey on Alcohol and Related Conditions III. JAMA 
Psychiatry, 72(8):757-766.  
5. 
Centers for Disease Control and Prevention. Excessive drinking costs U.S. $223.5 billion. Available 
at: http://www.cdc.gov/features/alcoholconsumption/. 
6. 
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th 
ed.). Arlington, VA: American Psychiatric Publishing. 
7. 
Kessler, R. C., Chiu, W. T., Demler, O., & Walters, E. E. (2005). Prevalence, severity, and comorbidity 
of 12-Month DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General 
Psychiatry, 62(6), 617-627.  
8. 
Institute of Medicine (2012). Treatment for Posttraumatic Stress Disorder in Military and Veteran 
Populations: Initial Assessment. Report Brief. Retrieved September 22, 2015 from www.iom.edu/ 
militiaryptsd. 
9. 
Smith, T. C., Ryan, M. K., Wingard, D. L., Slymen, D. J., Sallis, J. F., & Kritz-Silverstein, D. (2008). 
New onset and persistent symptoms of posttraumatic stress disorder self reported after deployment 
and combat exposures: Prospective population based US military cohort study. BMJ: British Medical 
Journal, 336(7640), 366-371.  
10. Back, S.E., Dansky, B. S., Coffey, S. F., Saladin, M. E., Sonne, S., & Brady, K. T. (2000). Cocaine 
dependence with and without posttraumatic stress disorder: A comparison of substance use, trauma 
history and psychiatric comorbidity. American Journal on Addictions, 9(1), 51-62.  
11. Petrakis, I.L., Rosenheck, R., & Desai, R. (2011). Substance use comorbidity among veterans with 
posttraumatic stress disorder and other psychiatric illness. American Journal on Addictions, 20(3), 
185–189. 
12. Torchalla, I., Nosen, L., Rostam, H., & Allen, P. (2012). Integrated treatment programs for individuals 
with concurrent substance use disorders and trauma experiences: A systematic review and meta-
analysis. Journal of Substance Abuse Treatment, (1), 65–77. 
13. van Dam, D., Vedel, E., Ehring, T., & Emmelkamp, P. G. (2012). Psychological treatments for 
concurrent posttraumatic stress disorder and substance use disorder: A systematic review. Clinical 
Psychology Review, 32(3), 202-214.  
14. Kessler R. C., Sonnega A., Bromet E., Hughes M., Nelson C. B. (1995). Posttraumatic stress disorder 
in the National Comorbidity Survey. Arch. Gen. Psychiatry 52, 1048–1060. 
15. Jacobsen, L. K., Southwick, S. M., & Kosten, T. R. (2001). Substance use disorders in patients with 
posttraumatic stress disorder: A review of the literature. American Journal of Psychiatry, 158(8), 1184-
1190.  
16. Gielen, N., Havermans, R. C., Tekelenburg, M., & Jansen, A. (2012). Prevalence of post-traumatic 
stress disorder among patients with substance use disorder: It is higher than clinicians think it is. 
European Journal of Psychotraumatology. doi:  10.3402/ejpt.v3i0.17734. 

 
Page 11 of 17 
 
17. Kehle, S. M., Reddy, M. K., Ferrier-Auerbach, A. G., Erbes, C. R., Arbisi, P. A., & Polusny, M. A. 
(2011). Psychiatric diagnoses, comorbidity, and functioning in National Guard troops deployed to Iraq. 
Journal of Psychiatric Research, 45(1), 126-132. 
18. Khantzian, E. J. (1985). Psychotherapeutic interventions with substance abusers: The clinical context. 
Journal of Substance Abuse Treatment, 2(2), 83-88. 
19. Kushner, M.G. (2014). Seventy-five years of comorbidity research. Journal of Studies on Alcohol and 
Drugs, 75(17), 50-58.  
20. Leeies, M., Pagura, J., Sareen, J., & Bolton, J. M. (2010). The use of alcohol and drugs to self-
medicate symptoms of posttraumatic stress disorder. Depression and Anxiety, 27(8), 731-736 
21. Ouimette, P., Read, J. P., Wade, M., & Tirone, V. (2010). Modeling associations between 
posttraumatic stress symptoms and substance use. Addictive Behaviors, 35(1), 64-67.  
22. Back, S.E., Jackson, J. L., Sonne, S., & Brady, K. T. (2005). Alcohol dependence and posttraumatic 
stress disorder: Differences in clinical presentation and response to cognitive-behavioral therapy by 
order of onset. Journal of Substance Abuse Treatment, 29(1), 29-37. 
23. Back, S. E. (2010). Toward an improved model of treating co-occurring PTSD and substance use 
disorders. American Journal of Psychiatry, 167(1), 11-13. 
24. Ouimette, P., Read, J., & Brown, P. J. (2005). Consistency of retrospective reports of DSM-IV criterion 
A traumatic stressors among substance use disorder patients. Journal of Traumatic Stress, 18(1), 43-
51. 
25. Pennington, D. L., Abé, C., Batki, S. L., & Meyerhoff, D. J. (2014). A preliminary examination of cortical 
neurotransmitter levels associated with heavy drinking in posttraumatic stress disorder. Psychiatry 
Research: Neuroimaging, 224(3), 281-287. 
26. Blanco, C., Xu, Y., Brady, K., Pérez-Fuentes, G., Okuda, M., & Wang, S. (2013). Comorbidity of 
posttraumatic stress disorder with alcohol dependence among US adults: Results from National 
Epidemiological Survey on Alcohol and Related Conditions. Drug and Alcohol Dependence, 132(3), 
630-638.  
27. Hellmuth, J.C., Teer, A., Beylotte, F.M., Killeen, T., & Back, S.E. (2015). Correlates of recent and 
lifetime aggression among treatment-seeking veterans dually diagnosed with posttraumatic stress and 
substance use disorders. Mental Health and Substance Use, 7(4), 315-328. 
28. Mills, K. L., Ewer, P., Dore, G., Teesson, M., Baker, A., Kay-Lambkin, F., & Sannibale, C. (2014). The 
feasibility and acceptability of a brief intervention for clients of substance use services experiencing 
symptoms of post traumatic stress disorder. Addictive Behaviors, 39(6), 1094-1099. 
29. Young, H. E., Rosen, C. S., & Finney, J. W. (2005). A survey of PTSD screening and referral practices 
in VA addiction treatment programs. Journal of Substance Abuse Treatment, 28(4), 313-319. 
30. Back, S. E., Brady, K. T., Sonne, S. C., & Verduin, M. L. (2006). Symptom Improvement in Co-
Occurring PTSD and Alcohol Dependence. Journal of Nervous and Mental Disease, 194(9), 690-696.  
31.  Badour, C.L., Flanagan, J. C., Gros, D. F., Killeen, T., Pericot-Valverde, I., Korte, K.J., & Back, S.E. 
(under review). Habituation of distress and craving as predictors of change in PTSD symptoms and 
Substance use during integrated treatment. 
32. McLean, C. P., Su, Y., & Foa, E. B. (2015). Mechanisms of symptom reduction in a combined 
treatment for comorbid posttraumatic stress disorder and alcohol dependence. Journal of Consulting 
and Clinical Psychology, 83(3), 655-661. 
33. Back, S. E., Killeen, T. K., Teer, A. P., Hartwell, E. E., Federline, A., Beylotte, F., & Cox, E. (2014). 
Substance use disorders and PTSD: An exploratory study of treatment preferences among military 
veterans. Addictive Behaviors, 39(2), 369-373.  

 
Page 12 of 17 
 
34. Beck, A., Wüstenberg, T., Genauck, A., Wrase, J., Schlagenhauf, F., Smolka, M. N., & ... Heinz, A. 
(2012). Effect of brain structure, brain function, and brain connectivity on relapse in alcohol-dependent 
patients. JAMA Psychiatry, 69(8), 842-853.  
35. Rolls, E. T. (2000). The orbitofrontal cortex and reward. Cerebral Cortex, 10(3), 284-294. 
36. Worhunsky, P. D., Stevens, M. C., Carroll, K. M., Rounsaville, B. J., Calhoun, V. D., Pearlson, G. D., 
& Potenza, M. N. (2013). Functional brain networks associated with cognitive control, cocaine 
dependence, and treatment outcome. Psychology of Addictive Behaviors, 27(2), 477-488.  
37. Pitman, R. K., Rasmusson, A. M., Koenen, K. C., Shin, L. M., Orr, S. P., Gilbertson, M. W., & Liberzon, 
I. (2012). Biological studies of post-traumatic stress disorder. Nature Reviews Neuroscience, 13(11), 
769-787.  
38. Schneider, F., Habel, U., Wagner, M., Franke, P., Salloum, J. B., Shah, N. J., & ... Zilles, K. (2001). 
Subcortical correlates of craving in recently abstinent alcoholic patients. American Journal of 
Psychiatry, 158(7), 1075-1083. 
39. Norman, G. J., Hawkley, L., Luhmann, M., Ball, A. B., Cole, S. W., Berntson, G. G., & Cacioppo, J. T. 
(2012). Variation in the oxytocin receptor gene influences neurocardiac reactivity to social stress and 
HPA function: A population based study. Hormones and Behavior, 61(1), 134-139. 
40. Winstanley, C.A., Theobald, D.E.H., Cardinal, R.N., & Robbins, T.W. (2004). Contrasting roles of 
basolateral amygdala and orbitofrontal cortex in impulsive choice. Journal of Neuroscience, 
24(20):4718–4722. 
41. Sinha, R., & Li, C. R. (2007). Imaging stress- and cue-induced drug and alcohol craving: Association 
with relapse and clinical implications. Drug and Alcohol Review, 26(1), 25-31. 
42. Zhang, Y., Tian, J., Yuan, K., Liu, P., Zhuo, L., Qin, W., & Liu, Y. (2011).  Distinct resting-state brain 
activities in heroin-dependent individuals.  Brain Research, 1402, 46-53. 
43. Dayas, C. V., Liu, X., Simms, J. A., & Weiss, F. (2007). Distinct Patterns of Neural Activation 
Associated with Ethanol Seeking: Effects of Naltrexone. Biological Psychiatry, 61(8), 979-989.  
44. Benegal V, Antony G, Venkatasubramanian G, Jayakumar PN. (2007). Gray matter volume 
abnormalities and externalizing symptoms in subjects at high risk for alcohol dependence. Addict Biol, 
12:122–132. 
45. Wrase, J., Makris, N., Braus, D. F., Mann, K., Smolka, M. N., Kennedy, D. N., & z, A. (2008). Amygdala 
volume associated with alcohol abuse relapse and craving. American Journal of Psychiatry, 165(9), 
1179-1184.  
46. Huang, M.X., Yurgil, K.A., Robb, A., Angeles, A., Diwakar, M., Risbrough, V.B., Nichols, S.L., McLay, 
R., Theilmann, R.J., Song, T., Huang, C.W., Lee, R.R., Baker, D.G., (2014). Voxel-wise resting-state 
MEG source magnitude imaging study reveals neurocircuitry abnormality in active-duty service 
members and veterans with PTSD. NeuroImage: Clinical, vol. 5, pp. 408-419. 
47. Holmes, A., Fitzgerald, P. J., MacPherson, K. P., DeBrouse, L., Colacicco, G., Flynn, S. M., & ... 
Camp, M. (2012). Chronic alcohol remodels prefrontal neurons and disrupts NMDAR-mediated fear 
extinction encoding. Nature Neuroscience, 15(10), 1359-1361. 
48. Sinha, R. (2011). New findings on biological factors predicting addiction relapse vulnerability. Current 
Psychiatry Reports, 13(5), 398-405. 
49. McHugh, M. J., Demers, C. H., Salmeron, B. J., Devous, M. S., Stein, E. A., & Adinoff, B. (2014). 
Cortico-amygdala coupling as a marker of early relapse risk in cocaine-addicted individuals. Frontiers 
in Psychiatry, 5: 16. 
50. Sripada, R. K., King, A. P., Welsh, R. C., Garfinkel, S. N., Wang, X., Sripada, C. S., & Liberzon, I. 
(2012). Neural dysregulation in posttraumatic stress disorder: Evidence for disrupted equilibrium 
between salience and default mode brain networks. Psychosomatic Medicine, 74(9), 904-911. 

 
Page 13 of 17 
 
51. Myers-Schulz, B., & Koenigs, M. (2012). Functional anatomy of ventromedial prefrontal cortex: 
Implications for mood and anxiety disorders. Molecular Psychiatry, 17(2), 132-141. 
52. Milad, M. R., Pitman, R. K., Ellis, C. B., Gold, A. L., Shin, L. M., Lasko, N. B., & ... Rauch, S. L. (2009). 
Neurobiological basis of failure to recall extinction memory in posttraumatic stress disorder. Biological 
Psychiatry, 66(12), 1075-1082. 
53. Bremner JD, Elzinga B, Schmahl C, Vermetten E.(2008). Structural and functional plasticity of the 
human brain in posttraumatic stress disorder. Prog Brain Res, 167, 171–86. 
54. Shin, L. M., & Handwerger, K. (2009). Is posttraumatic stress disorder a stress-induced fear circuitry 
disorder? Journal of Traumatic Stress, 22(5), 409-415.  
55. Goldstein, R. Z., & Volkow, N. D. (2011). Dysfunction of the prefrontal cortex in addiction: 
Neuroimaging findings and clinical implications. Nature Reviews Neuroscience, 12(11), 652-669.  
56. Niciu, M. J., Kelmendi, B., & Sanacora, G. (2012). Overview of glutamatergic neurotransmission in 
the nervous system. Pharmacology, Biochemistry and Behavior, 100(4), 656-664. 
57. Myers, K. M., Carlezon, W. J., & Davis, M. (2011). Glutamate receptors in extinction and extinction-
based therapies for psychiatric illness. Neuropsychopharmacology, 36(1), 274-293. 
58. Bermudo-Soriano, C. R., Perez-Rodriguez, M. M., Vaquero-Lorenzo, C., & Baca-Garcia, E. (2012). 
New perspectives in glutamate and anxiety. Pharmacology, Biochemistry and Behavior, 100(4), 752-
774.  
59. Nair, J., & Ajit, S. S. (2008). The role of the glutamatergic system in posttraumatic stress disorder. 
CNS Spectrums, 13(7), 585-591. 
60. Becquet, D., Hery, M., Francois-Bellan, A. M., Giraud, P., Deprez, P., Faudon, M., et al. (1993). 
Glutamate,GABA, glycine and taurine modulatesero- tonin synthesis and release in rostral and caudal 
rhombencephalic raphe cells in primary cultures. Neurochem. Int. 23, 269–283.  
61. Jedema, H.P. Moghddam, B. (1996). Characterization of excitatory amino acid modulation of 
dopamine release in the prefrontal cortex of conscious rats. J Neurochem, 66, 1448–1453. 
62. Oscar-Berman, M. & Marinkovic, K. (2007). Alcohol: Effects on neurobehavioral functions and the 
brain. Neuropsychological Review, 17(3), 239-257. 
63. Gass, J.T. & Olive, M.F. (2008). Glutamatergic substrates of drug addiction and alcoholism. 
Biochemical Pharmacology, 75, 218–265. 
64. Reissner, K. J., Gipson, C. D., Tran, P. K., Knackstedt, L. A., Scofield, M. D., & Kalivas, P. W. (2015). 
Glutamate transporter GLT-1 mediates N‐acetylcysteine inhibition of cocaine reinstatement. Addiction 
Biology, 20(2), 316-323. 
65. Wolfe, D.J. & Kalivas, P.W. (2015). Glutamate transporter GLT-1 as a therapeutic target for substance 
use disorders. CNS Neurol Disord Drug Targets, 14(6), 745-56. 
66. Moussawi, K., Zhou, W., Shen, H., Reichel, C.M., See, R.E., Carr, D.B., et al. (2011). Reversing 
cocaine-induced synaptic potentiation provides enduring protection from relapse. Proc Natl Acad Sci 
U S A, 108, 385-390. 
67. Knackstedt, L. A., LaRowe, S., Mardikian, P., Malcolm, R., Upadhyaya, H., Hedden, S., & ... Kalivas, 
P. W. (2009). The role of cystine-glutamate exchange in nicotine dependence in rats and humans. 
Biological Psychiatry, 65(10), 841-845. 
68. Sari Y., Sakai M., Weedman J. M., Rebec G. V., Bell R. L. (2011). Ceftriaxone, a beta-lactam antibiotic, 
reduces ethanol consumption in alcohol-preferring rats. Alcohol Alcohol. 46, 239–246. 
69. Ramirez-Niño, A. M., D’Souza, M. S., & Markou, A. (2013). N-acetylcysteine decreased nicotine self-
administration and cue-induced reinstatement of nicotine seeking in rats: Comparison with the effects 
of N-acetylcysteine on food responding and food seeking. Psychopharmacology, 225(2), 473-482. 

 
Page 14 of 17 
 
70. Reissner, K.J., Brown, R.M., Spencer, S., Tran, P.K., Thomas, C.A., & Kalivas, P.W. (2014). Chronic 
administration of the methylxanthine propentofylline impairs reinstatement to cocaine by a GLT-1-
dependent mechanism. Neuropsychopharmacology, 39, 499–506.  
71. Sofuoglu, M., DeVito, E. E., Waters, A. J., & Carroll, K. M. (2013). Cognitive enhancement as a 
treatment for drug addictions. Neuropharmacology, 64, 452-463. 
72. Kalivas, P. W., & Volkow, N. D. (2011). New medications for drug addiction hiding in glutamatergic 
neuroplasticity. Molecular Psychiatry, 16(10), 974-986.  
73. Aupperle, R. L., Allard, C. B., Grimes, E. M., Simmons, A. N., Flagan, T., Behrooznia, M., & ... Stein, 
M. B. (2012). Dorsolateral prefrontal cortex activation during emotional anticipation and 
neuropsychological performance in posttraumatic stress disorder. Archives of General Psychiatry, 
69(4), 360-371.  
74. Koob, G. & Volkow, N. (2010). Neurocircuitry of addiction. Neuropsychopharmacology, 35(1), 217-
238.  
75. Cortese BM, Phan LK. (2005). The role of glutamate in anxiety and related disorders CNS. Spectr, 
10(10), 820–830. 
76. Batki, S. L., Pennington, D. L., Lasher, B., Neylan, T. C., Metzler, T., Waldrop, A., & Herbst, E. (2014). 
Topiramate treatment of alcohol use disorder in veterans with posttraumatic stress disorder: A 
randomized controlled pilot trial. Alcoholism: Clinical and Experimental Research, 38(8), 2169-2177. 
77. Brady, K. T., Sonne, S., Anton, R. F., Randall, C. L., Back, S. E., & Simpson, K. (2005). Sertraline in 
the treatment of co-occurring alcohol dependence and posttraumatic stress disorder. Alcoholism: 
Clinical and Experimental Research, 29(3), 395-401. 
78. Foa, E. B., Yusko, D. A., McLean, C. P., Suvak, M. K., Bux, D. J., Oslin, D., & ... Volpicelli, J. (2013). 
Concurrent naltrexone and prolonged exposure therapy for patients with comorbid alcohol 
dependence and PTSD: A randomized clinical trial. JAMA, 310(5), 488-495.  
79. Hien, D. A., Levin, F. R., Ruglass, L. M., Lopez-Castro, T., Papini, S., Hu, M. C., . . . Herron, A. (2015). 
Combining seeking safety with sertraline for PTSD and alcohol use disorders: A randomized controlled 
trial. J Consult Clin Psychol, 83(2), 359-369. 
80. Petrakis, I. L., Ralevski, E., Desai, N., Trevisan, L., Gueorguieva, R., Rounsaville, B., & Krystal, J. H. 
(2012). Noradrenergic vs serotonergic antidepressant with or without naltrexone for veterans with 
PTSD and comorbid alcohol dependence. Neuropsychopharmacology, 37(4), 996-1004.  
81. Sofuoglu, M., Rosenheck, R., & Petrakis, I. (2014). Pharmacological treatment of comorbid PTSD and 
substance use disorder: Recent progress. Addictive Behaviors, 39(2), 428-433. 
82. Moran, M. M., McFarland, K., Melendez, R. I., Kalivas, P. W., & Seamans, J. K. (2005). 
Cystine/Glutamate Exchange Regulates Metabotropic Glutamate Receptor Presynaptic Inhibition of 
Excitatory Transmission and Vulnerability to Cocaine Seeking. Journal of Neuroscience, 25(27), 6389-
6393. 
83. Olive, M.F., Cleva, R.M., Kalivas, P.M., & Malcom, R.J. (2012). Glutamatergic medications for the 
treatment of drug and behavioral addictions. Pharmachology, Biochemistry and Behavior, 100, 801-
810. 
84. Amen, S. L., Piacentine, L. B., Ahmad, M. E., Li, S., Mantsch, J. R., Risinger, R. C., & Baker, D. A. 
(2011). Repeated N-acetyl cysteine reduces cocaine seeking in rodents and craving in cocaine-
dependent humans. Neuropsychopharmacology, 36(4), 871-878. 
85. Gass JT, Sinclair CM, Cleva RM, Widholm JJ, Olive MF. (2011). Alcohol-seeking behavior is 
associated with increased glutamate transmission in basolateral amygdala and nucleus accumbens 
as measured by glutamate-oxidase-coated biosensors. Addict Biol, 16(2), 215–28. 

 
Page 15 of 17 
 
86. Garcia-Keller C, Martinez SA, Esparza MA, Bollati F, Kalivas PW, Cancela LM. Cross-sensitization 
between cocaine and acute restraint stress is associated with sensitized dopamine but not glutamate 
release in the nucleus accumbens. Eur J Neurosci 37, 982-995. 
87. Gipson, C. D., Reissner, K. J., Kupchik, Y. M., Smith, A. W., Stankeviciute, N., Hensley-Simon, M. E., 
& Kalivas, P. W. (2013). Reinstatement of nicotine seeking is mediated by glutamatergic plasticity. 
PNAS Proceedings of The National Academy of Science, 110(22), 9124-9129.  
88. Knackstedt, L. A., Moussawi, K., Lalumiere, R., Schwendt, M., Klugmann, M., & Kalivas, P. W. (2010). 
Extinction training after cocaine self-administration induces glutamatergic plasticity to inhibit cocaine 
seeking. The Journal Of Neuroscience, 30(23), 7984-7992. 
89. Schneider, R. J., Santos, C. F., Clarimundo, V., Dalmaz, C., Elisabetsky, E., & Gomez, R. (2015).            
N-acetylcysteine prevents behavioral and biochemical changes induced by alcohol cessation in rats. 
Alcohol, 49(3), 259-263.  
90. Gray, K. M., Carpenter, M. J., Baker, N. L., DeSantis, S. M., Kryway, E., Hartwell, K. J., & ... Brady, 
K. T. (2012). A double-blind randomized controlled trial of N-acetylcysteine in cannabis-dependent 
adolescents. American Journal of Psychiatry, 169(8), 805-812.  
91. LaRowe, S. D., Kalivas, P. W., Nicholas, J. S., Randall, P. K., Mardikian, P. N., & Malcolm, R. J. 
(2013). A double‐blind placebo‐controlled trial of N‐acetylcysteine in the treatment of cocaine 
dependence. American Journal on Addictions, 22(5), 443-452.  
92. Schmaal, L., Veltman, D. J., Nederveen, A., van den Brink, W., & Goudriaan, A. E. (2012). N-
acetylcysteine normalized glutamate levels in cocaine-dependent patients: A randomized crossover 
magnetic resonance spectroscopy study. Neuropsycho- pharmacology, 37(9), 2143-2152. 
93. Afshar, H., Roohafza, H., Mohammad-Beigi, H., Haghighi, M., Jahangard, L., Shokouh, P., & ... 
Hafezian, H. (2012). N-acetylcysteine add-on treatment in refractory obsessive-compulsive disorder: 
A randomized, double-blind, placebo-controlled trial. Journal of Clinical Psychopharmacology, 32(6), 
797-803.  
94. Dean, O., Giorlando, F., & Berk, M. (2011). N-acetylcysteine in psychiatry: Current therapeutic 
evidence and potential mechanisms of action. Journal of Psychiatry & Neuroscience, 36(2), 78-86.  
95. Grandjean, E.M., Berthet, .P, Ruffmann, R., & Leuenberger, P. (2000). Efficacy of oral long-term N-
acetylcycteine in chronic bronchopulmonary disease: a meta-analysis of published double-blind, 
placebo-controlled clinical trials. Clin Ther, 22(2), 209–221. 
96. Smilkstein MJ, Knapp GL, Kulig KW, Rumack BH. (1988). Efficacy of oral N-acetylcysteine in the 
treatment of acetaminophen overdose. Analysis of the national multicenter study (1976 to 1985). N 
Engl J Med,319, 1557–1562. 
97. McClure, E. A., Baker, N. L., Gipson, C. D., Carpenter, M. J., Roper, A. P., Froeliger, B. E., &  Gray, 
K. M. (2015). An open-label pilot trial of N-acetylcysteine and varenicline in adult cigarette smokers. 
American Journal of Drug And Alcohol Abuse, 41(1), 52-56. 
98. Nikoo, M., Radnia, H., Farokhnia, M., Mohammadi, M., & Akhondzadeh, S. (2015). N-acetylcysteine 
as an adjunctive therapy to risperidone for treatment of irritability in autism: A randomized, double-
blind, placebo-controlled clinical trial of efficacy and safety. Clinical Neuropharmacology, 38(1), 11-
17.  
99. Marzullo, L. (2005). An update of N-acetylcysteine treatment for acute acetaminophen toxicity in 
children. Curr Opin Pediatr, 17, 239–245. 
100. Gray, K. M., Watson, N.L., Carpenter, M.J., & Larowe, S.D. (2010).  N-acetylcysteine (NAC) in young 
marijuana users: An open-label pilot study. Am J Addict, 19, 187–9. 
101. Hien, D. A., Jiang, H., Campbell, A. N., Hu, M. C., Miele, G. M., Cohen, L. R., . . . Nunes,  E. V. (2010). 
Do treatment improvements in PTSD severity affect substance use outcomes? A secondary analysis 
from a randomized clinical trial in NIDA's Clinical Trials Network. Am J Psychiatry, 167(1), 95-101.  

 
Page 16 of 17 
 
102. LaRowe, S.D., Myrick, H, Hedden, S., Mardikian, P., Saladin, M., McRae, A., . . .    Malcom, R. (2007). 
Is cocaine desire reduced by n-acetylcysteine? American Journal of   Psychiatry, 164, 1115-1117. 
103. Back, S.E., McCauley, J.L., Korte, K.J.., Gros, D.F., Leavitt, V., Gray, K., Hamner, M.,  Malcolm, R., 
Brady, K.T., & Kalivas, P. (under review). A double-blind randomized controlled pilot trial of N-
acetylcysteine in Veterans with PTSD and substance use   disorders. 
104. Back, S.E., Waldrop, A. E., & Brady, K. T. (2009). Treatment challenges associated with comorbid 
substance use and posttraumatic stress disorder: Clinicians’ perspectives. American Journal on 
Addictions, 18(1), 15-20.  
105. Schacht, J. P., Anton, R. F., & Myrick, H. (2013). Functional neuroimaging studies of alcohol cue 
reactivity: A quantitative meta‐analysis and systematic review. Addiction  Biology, 18(1), 121-133.  
106. Seeley, W.W., Menon, V., Schatzberg, A.F., Keller, J., Glover, G.H., Kenna, H., . . .   Greicious, M.D. 
(2007). Dissociable intrinsic connectivity networks for salience processing and executive control. 
Journal of Neuroscience, 27(9), 2349–2356.  
107. Anton, R.F., Lieber, C., & Tabakoff, B. (2002). Carbohydrate-deficient transferrin and γ- 
glutamyltransferase for the detection and monitoring of alcohol use: Results from a multisite study. 
Alcoholism: Clinical and Experimental Research, 26(8), 1215-1222.  
108. Litten, R. Z., & Allen, J. P. (1995). Pharmacotherapy for alcoholics with collateral depression or 
anxiety: An update of research findings. Experimental and Clinical  Psychopharmacology, 3(1), 87-
93.  
109. Litten, R.Z., Bradley, A.M., & Moss, H.B. (2010). Alcohol biomarkers in applied settings: recent 
advances and future research opportunities. Alcohol Clin Exp Res, 34(6), 955–967. 
110.  Lowe, J. M., McDonell, M. G., Leickly, E., Angelo, F. A., Vilardaga, R., McPherson, S.,Srebnik, D., 
Roll, J., & Ries, R. K. (2015). Determining ethyl glucuronide cutoffs when  detecting self-reported 
alcohol use in addiction treatment patients. Alcohol Clin Exp Res, 39(5), 905-910.  
111. Sobell, L. C., & Sobell, M. B. (1992). Timeline follow-back: A technique for assessing self-reported 
alcohol consumption. In R. Z. Litten, & J. P. Allen (Eds.), Measuring Alcohol Consumption: 
Psychosocial and Biochemical Methods (pp 41-72). Humana Press. 
112. Carey, K. B. (1997). Reliability and validity of the time-line follow-back interview among psychiatric 
outpatients: A preliminary report. Psychology of Addictive Behaviors, 11(1), 26-33. 
113.  Anton, R. F., Moak, D. H., & Latham, P. (1995). The Obsessive Compulsive Drinking Scale: A self-
rated instrument for the quantification of thoughts about alcohol and drinking behavior. Alcoholism: 
Clinical and Experimental Research, 19(1), 92-99. 
114. Drobes, D. J., & Thomas, S. E. (1999). Assessing craving for alcohol. Alcohol Research & Health, 
23(3), 179-186.  
115. Weathers, F. W., Marx, B. P., Friedman, M. J., & Schnurr, P. P. (2014). Posttraumatic  stress disorder 
in DSM-5: New criteria, new measures, and implications for assessment. Psychological Injury and 
Law, 7(2), 93-107.  
116. Weathers, F. W., Keane, T. M., & Davidson, J. T. (2001). Clinician-Administered PTSD Scale: A review 
of the first ten years of research. Depression and Anxiety, 13(3), 132-156.  
117. McDonald, S. D., & Calhoun, P. S. (2010). The diagnostic accuracy of the PTSD Checklist: A critical 
review. Clinical Psychology Review, 30(8), 976-987. 
118. Kadden, R. Carroll, K., Donovan, D., Cooney, N., Monti, P., Abrams, D. Litt, M. & Hester, R. (1992).  
Cognitive-Behavioral Coping Skills Therapy Manual: A Clinical Research Guide for Therapists 
Treating Individuals with Alcohol Abuse and Dependence. NIAAA Project MATCH Monograph Series 
Vol. 3. DHHS Pub. No. (ADM) 92-1895. Rockville, MD: National Institute on Alcohol Abuse and 
Alcoholism.  

 
Page 17 of 17 
 
119. Carroll, K. M., Kosten, T. R., & Rounsaville, B. J. (2004). Choosing a behavioral therapy platform for 
pharmacotherapy of substance users. Drug and Alcohol Dependence, 75(2), 123-134.  
120. DeRubeis, R. J., Hollon, S. D., Evans, M. D., & Bemis, K. M. (1982). Can psychotherapies for 
depression be discriminated? A systematic investigation of cognitive therapy and interpersonal 
therapy. Journal of Consulting and Clinical Psychology, 50(5), 744-756.  
121. Holdiness, M.R., (1991). Clinical Pharmacokinetics of N-acetylcysteine. Clin Pharmacokinet, 20(2), 
123-134. 
122. Zhou, J. Coles, L.D., Kartha, R.V., Nash, N., Mishra, U., Lund, T.C., & Cloyd, J.C. (2015). Intravenous 
administration of stable-labeled N-Acetylcysteine demonstrates an indirect mechanism for boosting 
glutathione and improving redox status. Journal of Pharmaceutical Sciences, 104(8), 2619-2626. 
123. Mullins, P.G., Chen, H., Xu, J., Caprihan, A., & Gasparovic, C. (2008). Comparative reliability of proton 
spectroscopy techniques designed to improve detection of J-coupled metabolites. Magnetic 
Resonance in Medicine, 60, 964-969. 
124. Bandyopadhyay, D., DeSantis, S. M., Korte, J. E., & Brady, K. T. (2011). Some considerations for 
excess zeroes in substance abuse research. American Journal of Drug and Alcohol Abuse, 37(5), 
376-382. 
125. Cragg, J.G. (1971). Some statistical models for limited dependent variables with application to the 
demand for durable goods. Econometrica, 39(5), 829-44. 
126. Vuong, Q.H. (1989). Likelihood ratio tests for model selection and non tested hypotheses. 
Econometrica, 57(2), 307-333. 
127. Power J. D., Barnes K. A., Snyder A. Z., Schlaggar B. L., Petersen S. E. (2012).  Spurious but 
systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 
59, 2142–2154. 
128. Friston, K. J., Buechel, C., Fink, G. R., Morris, J., Rolls, E., & Dolan, R. J. (1997). Psychophysiological 
and modulatory interactions in neuroimaging. Neuroimage, 6(3), 218-229.  
129.  Moran-Santa Maria, M. M., Hartwell, K. J., Hanlon, C. A., Canterberry, M., Lematty, T., Owens, M., . 
. . George, M. S. (2015). Right anterior insula connectivity is important for cue-induced craving in 
nicotine-dependent smokers. Addict Biol, 20(2), 407-414. 
130. Provencher, S.W. (1993). Estimation of metabolite concentrations from localized in vivo proton NMR 
spectra. Magnetic Resonance in Medicine, 30, 672-679.