QuitFast: Evaluating Transcranial Magnetic Stimulation as a Tool to Reduce Smoking Directly Following a Quit Attempt
Phase: NA
Conditions: Smoking, Smoking Cessation, Craving, Addiction, Addiction Nicotine, Nicotine Dependence, Cigarette Smoking
Sponsor: Wake Forest University Health Sciences

Protocol version: 4.4 
Protocol date: 06/10/2022 
1 
Study Title: QuitFast: Evaluating transcranial magnetic stimulation as a tool to reduce smoking 
directly following a quit attempt 
 
Principal Investigator, Co-investigator(s): Colleen Hanlon, PhD; Eric Donny, PhD; Heather Douglas, 
MD 
 
Sponsor or funding source: NIH/NIDA 
 
Background, Rationale and Context 
Significance 1- Developing a novel treatment tool targeted at the first 2 weeks after a smoking quit attempt. 
Smoking is the leading preventable cause of mortality and morbidity in the United States, contributing to  
approximately 443,000 deaths annually -  more than the deaths attributable to alcohol, illicit-drug use, 
homicide, AIDS, and suicide combined (14). The combined health and loss-of-productivity costs associated 
with smoking are substantial, exceeding 300 billion dollars per year, or nearly 10 times NIH’s entire 2016 
budget (14, 15). In 2015, approximately 36.5 million Americans (15%) were regular smokers (“at least 100 
cigarettes total and presently smoking most days of the week”). Of regular smokers 68% would like to quit 
smoking, and 43% had initiated a smoking quit attempt in the 
previous year (15.7 million Americans). While these numbers 
suggest that individuals are motivated to quit smoking, only 6% 
of smokers who attempt to quit without assistance maintain 
abstinence for 30 days (14). A Cochrane Review of 
demonstrated that current smoking cessation treatments (e.g., 
nicotine replacement therapy, cognitive behavioral therapy 
[CBT], non-nicotine medications) are 2-3 times more effective 
than quitting without assistance, however their aggregate 
success rate is only 30% (70% failure rate) (16).   
The most critical period for long-term success of smoking 
cessation appears to be in the first 7 days after the quit date.  A 
metaanalyses of 3 independent pharmacotherapy trials revealed 
that abstinence during the first 7 days was the strongest predictor 
of 6-month treatment outcomes (n=1649; Odds ratio: 1.4, P 
<0.0001; Ashare et al. 2013 (110)). High relapse rates during this 
first week of smoking cessation are likely facilitated by 
behavioral and neurobiological factors that contribute to cue-
associated craving and loss of executive control. This statement 
is backed by over 20 years of research. The long term goal of 
the research is to develop non-invasive brain stimulation as 
an evidence-based tool to facilitate abstinence during this 
critical period after a quit attempt.  The scientific premise is 
that by modulating the neural circuits with govern cue-associated craving and executive control, it 
may be possible to get people past this vulnerable period for relapse.  Our long term vision is that this 
early brain stimulation intervention would improve long term outcomes of other pharmacotherapeutic and 
behavioral training approaches – a topic we will be prepared to explore in a subsequent renewal of this R01 
proposal. The goal of the present proposal is to evaluate 2 promising brain stimulation treatment 
strategies as innovative new tools which can modulate the neural circuits that contribute to relapse, 
and enable individuals to get through this critical period. 
Significance 2- Using basic science knowledge to developing an effective, neural-circuit based treatment 
for tobacco use. Through technical and experimental advances in preclinical neuroscience research over the 
last 10 years, we have an increasingly sophisticated understanding of the neural circuitry of substance 
Figure 1. A representation showing the 
separation and unique targets of the fronto-
striatal loops in the non-human primate, 
adapted from Haber and Knutson, 2010, 
Neuropsychopharmacology (1) 

Protocol version: 4.4 
Protocol date: 06/10/2022 
2 
dependence. Through optogenetics (17, 18) and designer receptors exclusively activated by designer drugs 
(DREADDs; (19)) it is possible to change drug taking behavior through direct stimulation of frontal-striatal 
circuits.  Specifically, stimulation of the prelimbic cortex (PL) leads to an increase in drug taking, whereas, 
stimulation of the infralimbic cortex (IL) will decrease drug taking (20). The PL is functionally and 
anatomically similar to the ventral medial prefrontal cortex (vmPFC) (Fig1. Red), whereas the IL is 
functionally similar the human dorsal lateral prefrontal cortex (dlPFC) (Fig. 1, yellow) (21, 22).  
Given these promising preclinical data, there is strong momentum to develop a neural circuit-based 
treatment for clinical substance abuse.  Transcranial magnetic stimulation (TMS) allows researchers to 
selectively activate or inhibit populations of neurons in humans. Through electromagnetic induction, 
repetitive pulses of TMS to the scalp will induce long-term potentiation-like (LTP-like) or long-term 
depression-like (LTD-like) effects in the cortical area beneath the coil in a frequency-dependent manner.  
Repetitive TMS to the frontal cortex induces a change in dopamine binding (23, 24) in monosynaptic striatal 
targets.   By applying 
either a single high 
frequency (> 10 Hz) or 
intermittent 
bursting 
frequency (intermittent 
theta burst stimulation; 
iTBS) to the cortex, it is 
possible to induce an 
LTP-like effect on both 
behavior and neural 
activity (23,25) (26). 
By applying either a 
single low frequency 
(1-5 Hz) or continuous 
bursting 
frequency 
(cTBS), it is possible 
to induce an LTD like 
effect on behavior and 
neural activity (3). In 
2013 
our 
group 
differentially activated 
these 
two 
frontal-
striatal circuits in healthy adults using TMS (10). In 2016 this was replicated and extended to cocaine users 
all of whom were also tobacco users) (27).  
 
We have recently demonstrated the LTD-like effects of cTBS on the vmPFC, striatum, and insula in cocaine 
users (63,111), alcohol users (111), and smokers (Figure 5).  As a significant conceptual advance in the 
field, this proposal will parametrically evaluate 2 promising neural-circuit based strategies as tools 
to decrease cigarette use, demand, and brain reactivity to smoking cues. The results from this double 
blinded proposal will pave a clear pathway for the systematic development of these neural-circuit 
based strategies as treatments for our participants.  
 
Significance 3 – Using an established conceptual model in addiction as a foundation for brain stimulation 
treatment development. Transcranial magnetic stimulation (TMS) is an FDA-approved treatment for 
depression which is now used in over 650 clinics in all 50 states (Neuronetics Company data), and covered 
by Medicare in 48 states (Centers for Medicare and Medicaid Services, Local Coverage Determinations). 
The evolving availability of clinical devices and trained staff represents a latent public health resource with 
incredible potential. Through this network of devices, evidence based TMS protocols for substance 
Figure 2. Adapted from Hanlon et al 2015, A) The competing neurobehavioral decision 
systems (CNDS) theory posits that in addiction, choice results from imbalance between 
2 decision systems (impulsive and executive), which are functionally linked to limbic 
and executive control circuits in the brain (Bickel et al 2016). B) It follows then, by 
modulating these competing neural circuits with TMS (e.g. either dampening the 
limbic/impulsive system or amplifying the executive control system), we may be able to 
induce a sustainable change in smoking behavior.  

Protocol version: 4.4 
Protocol date: 06/10/2022 
3 
dependence, including tobacco use, could be swiftly distributed to the public. Currently however, the 
field of addiction does not have enough data to make a well-informed decision regarding the TMS 
strategy that is likely best suited for changing smoking behavior. As of June 2016, there were 12 
published studies (including our own) which evaluated TMS as a tool to decrease craving for cigarettes. Of 
the 12 studies, 9 applied high frequency stimulation (10hz, 20Hz, iTBS) to the left dorsolateral prefrontal 
cortex (dlPFC) (Strategy 2 in this proposal), the same target used in depression. While this may be valuable, 
as both diseases share common deficits in executive control (which is largely modulated by the dlPFC), 
scientifically, it is not obvious that the standard treatments for depression (e.g. 10Hz and iTBS to the 
left dlPFC) will be the optimal target sites for tobacco use. The neurobiological basis for these diseases 
are not identical. While these early studies are promising, without a cohesive scientific framework, it 
is very difficult to reconcile these small studies with one another. To make significant progress in 
TMS treatment development for tobacco use, we have proposed a large, rigorous, evidence-based 
study which will prospectively evaluate 2 TMS treatment strategies that logically flow from the 
Competing Neurobehavioral Decision System (CNDS) theory – an established conceptual model of 
addiction which unites both behavioral and brain systems involved in tobacco dependence (28-30).   
 
The CNDS specifies that the frontal-striatal circuits involved in limbic reward and impulsive action are 
relatively hyperactive, while the executive control circuits are relatively hypoactive. Intervention efforts 
could be therefore be directed at either decreasing impulsive reward circuit activity to cues or increasing 
the executive control circuit activity to cues. The primary goal of this proposal is to parametrically evaluate 
the efficacy of two promising new circuit-
based interventions for the treatment of 
cigarette smoking. Aim 1 will evaluate 
the efficacy of attenuating activity in the 
limbic reward system (responsible for 
reward valuation and craving, Figure 2). 
Aim 2 will evaluate the efficacy of 
amplifying activity in the executive 
control system (responsible for cognitive 
control, Figure 2). For both interventions 
we will evaluate the relative efficacy of 5, 
10, and 15 sessions of prefrontal cortex 
(PFC) theta burst stimulation (TBS), as a 
tool to change cigarette valuation, 
preference for immediate rewards (delay 
discounting), 
cigarette 
self-
administration, and the brain’s response 
to smoking cues.  
Significance 
4. 
Logical, 
outcome-
centered investigation of the TMS 
parameter space on smoking behavior and brain reactivity to cues. Despite the considerable enthusiasm for 
developing non-invasive brain stimulation treatment protocols for tobacco dependence, many unanswered 
questions remain. Prominent gaps in our knowledge include 1) the optimal target location [e.g. medial PFC 
(mPFC) or dlPFC], and 2) the durability of TMS treatment on smoking behavior. Arguably, these 
methodologic issues need to be resolved in a reasoned, parametric manner before launching large, 
expensive, multi-site studies. We have previously demonstrated that the 2 components of the CNDS theory 
(Impulsive/Limbic (mPFC & Executive control (dlPFC))) can be differentially stimulated with TMS [(10), 
Figure 3]. Moreover, activity in these circuits can be attenuated or amplified with human theta burst 
stimulation (TBS), a biologically-based form of TMS. Continuous TBS (cTBS) results in long-term 
depression (LTD) of cortical excitability and intermittent TBS (iTBS) results in long term potentiation 
Figure 3. Using interleaved TMS/BOLD imaging, we demonstrated that it 
is possible to differentiate medial and lateral frontal striatal circuits (pillars 
of the CNDS model) by applying TMS to two prefrontal targets: FP1 & F3 
of the EEG 10-20 system (activating MPFC and dlPFC) (Hanlon et al 
PlosOne 2013). 

Protocol version: 4.4 
Protocol date: 06/10/2022 
4 
(LTP) (3). The overarching goal of this proposal is to fill these gaps in our knowledge through a 5-
year parametric study investigating effects of LTP-like stimulation to the mPFC (Aim 1) and LTP-
like stimulation of dlPFC (Aim 2). The significance of this proposal is underscored not only by the 
innovative neural-circuit based approach to treatment development, but also by the logical, 
parametric methods we will use to evaluate the efficacy of these two potential interventions, which 
will inform future treatment studies.  
Significance 5. Use of evidence-based behavioral markers as dependent measures. Delay discounting, 
cigarette demand, self-administration, and cigarette craving each measure key independent processes 
related to daily smoking. First, in both cross-sectional and longitudinal studies, excessive discounting of 
delayed rewards is strongly associated with cigarette smoking and other substance use (31-35); for meta-
analyses see  (36, 37). In fact, excessive delay discounting is a candidate marker for addiction processes 
which can be used to identify and predict addiction and its severity, and predict failure in smoking cessation 
treatment severity (38-40). Moreover, emerging evidence suggests that experimental reductions in delay 
discounting in the laboratory are associated with reduced cigarette self-administration (41). Second, 
behavioral economic demand for a commodity, such as cigarettes, is an important indicator for potential 
harm (42). The interaction of two demand parameters, intensity of demand (i.e., total consumption when 
the commodity is free) and elasticity (i.e., the sensitivity of consumption to price increases) gleaned from 
a demand analysis, are measures of the value of cigarettes (43). Importantly, greater demand for cigarettes 
is associated with greater dependence severity (44) and is predictive of treatment outcome among smokers 
(greater discounting has worse outcomes) (39). Third, self-administration of cigarettes in the laboratory 
provides a detailed examination of experiment variables on smoking behavior in controlled environment 
(41). Fourth, state craving for cigarettes can be measured using the Questionnaire of Smoking Urges-Brief 
(QSU-Brief). Two factors derived from this questionnaire, relief from smoking and intention to smoke, are 
validated measures to assess momentary craving (45). These 
state measures allow for a comparison between self-reported 
craving and neural responsivity to the MRI cue reactivity 
task.  
Preliminary data demonstrating the systematic Development 
of Non-Invasive Brain Stimulation for Addiction.  
Over the last 4 years our laboratories at the Medical 
University of South Carolina (MUSC) and Virginia Tech 
Carillion Research Institute (VTCRI) have performed 3 
independent studies and 1 joint study which directly 
contribute to the rationale and design of the present proposal. 
While both of our research groups have a long history of 
publications which have contributed to the scientific thoughts 
that went into this proposal, the most germane studies are 
summarized below: 
In Study 1, we demonstrated that the frontal-striatal circuits 
involved in limbic drive and in executive control can be 
differentially activated through TMS in healthy, non-
smokers. In this study TMS was applied to a cortical node of 
the limbic circuit (frontal pole/vmPFC) and to the executive 
control circuit (dlPFC) while participants were in the MRI 
environment [(10) Figure 3]. Thus, demonstrating that TMS 
can selectively modulate limbic regions.  
In Study 2, we demonstrated that 6 sessions of LTD-like TMS (cTBS) to the vmPFC (Strategy 1 in the 
present proposal) delivered in a single day caused a specific decrease in orbitofrontal cortex and ventral 
Figure 5. In a pilot study by Dr. Hanlon & Bickel, after 5 
days of vmPFC cTBS (Strategy 1), individuals that 
received real TMS had a significant decrease in mPFC, 
insula and cingulate response to cigarette cues. This was 
not present in the Sham group. (Data shown are within 
group contrasts Visit 1 vs Visit 6, Factorial design, k = 25, 
voxel level p<0.05, uncorrected).  

Protocol version: 4.4 
Protocol date: 06/10/2022 
5 
striatal/accumbens BOLD signal in cocaine users (all of whom smoked cigarettes) relative to sham 
stimulation (Hanlon et al 2015). At the time, this was the first study to apply multiple doses of cTBS to the 
vmPFC.  
In Study 3, through a NIDA R21 Dr. Li (a co-investigator on this application), has evaluated the effect of 
10 sessions (10 days) of LTP-like TMS to the dlPFC (Strategy 2 in the present proposal, cigarette 
smokers) (10Hz rTMS) on 46 smokers randomized to receive real or sham TMS. These methods built on 
prior published work demonstrating single session effects on craving (7). The preliminary results of this 
study suggested that 10 days/10 sessions of standard 10 Hz rTMS is likely not sufficient to generate large 
quit rates after 1 month. Importantly however, several new reports have demonstrated the power of 
intermittent TBS (iTBS). iTBS is a biologically based treatment protocol, in which similar effect sizes can 
be achieved 20x faster relative to standard single frequency stimulation. Moreover, iTBS is as effective as 
10 Hz in depression (46), and multiple sessions per day are feasible) 
In Study 4, in a collaborative effort with the PI of this proposal (Hanlon) and Dr. Warren Bickel of Virginia 
Tech, we performed a sham-controlled randomized study evaluating the effects of 5 sessions of LTD-like 
cTBS TMS to the vmPFC on cigarette demand, delay discounting, and brain reactivity to cues in a cohort 
of 18 cigarette smokers. We demonstrated that this protocol decreased: (1) cigarette valuation as measured 
by behavioral economic demand (Figure 4), (2) delay discounting for monetary rewards, (3) the neural 
response to nicotine cues in the PFC, striatum, and insula (Figure 5), compared to a sham group. As we 
noted above, the current treatments available to smokers, although somewhat efficacious, are not robust. 
The 70% failure rate of treatment-as-usual highlights the necessity of exploring novel therapeutic 
interventions. Through a systematic and parametric exploration of the effect sizes and durability of 
two promising TMS treatment strategies, the experiments proposed in this 5-year R01 have the 
opportunity to begin a new chapter in the development of efficacious treatments for tobacco 
dependence.  
INNOVATION.  
Systematic Development of Non-Invasive Brain Stimulation for Addiction.  The development of brain 
stimulation treatments for addiction has garnered significant attention from both the NIH (i.e. recent 
RFAs) and in the literature [see reviews: (47-51)]. In humans, TMS is the only non-invasive tool 
currently available in human clinical research which can selectively inhibit limbic reward or potentiate 
executive control circuits that contribute to addiction. A single pulse of TMS applied over a cortical target 
induces significant changes in neural activity in the area directly under the coil as well as measurable 
changes in BOLD signal and dopamine levels in regions monosynaptically connected to the target (24, 
25, 52-56). Therefore, a series of repetitive TMS pulses can induce a sustainable potentiation or 
attenuation in those regions.  
Frequency. Nearly all brain stimulation studies published to date in addiction have used a single continuous 
frequency (often 10 Hz or 1 Hz) of stimulation (rather than a biologically relevant rhythm) in an attempt to 
modulate craving. While 10 Hz and 1 Hz are the oldest, most established, and most commonly used brain 
stimulation protocols, newer protocols based on biologically relevant rhythms have emerged (3) and appear 
to produce comparable effects, but in a more efficient manner than single frequency stimulation  (46). The 
most commonly used form of patterned brain stimulation is TBS. A single session of TBS stimulation (600 
total pulses delivered within 2 minutes in 3 pulse bursts with a 5Hz burst frequency) has effects that last up 
to an hour (3, 57). Modeled from basic science methods (58, 59), human TBS induces a potentiation and 
depression of cortical excitability when given in an intermittent (LTP-like) and continuous (LTD-like) 
manner, respectively (3). Given the role of LTP in the acquisition and maintenance of drug use behaviors, 
and the clinical reality that a more efficient treatment (2 minutes versus 20 minutes) would likely be more 
manageable for addiction treatment programs, in preliminary studies we have been using cTBS (rather than 
rTMS at 1 Hz) to induce an LTD-like state in limbic circuitry while nicotine users are viewing nicotine 
cues (see Figure 3 & 4).  

Protocol version: 4.4 
Protocol date: 06/10/2022 
6 
Target. Nearly all of the currently published brain stimulation studies in addiction have focused on the 
executive control circuit (dlPFC). Although several of these studies have found mixed results for nicotine 
(4, 6-8), the scientific rationale for targeting the dlPFC in addiction is sound and is typically based on its 
role in the executive processing system (likely necessary to resist drug cues). However, targeting the limbic 
reward system may be a more direct and efficacious approach to dampening cigarette valuation and cue-
reactivity given that nicotine directly affects dopamine reuptake in the mesolimbic dopamine system (60-
62). A recent sham-controlled crossover study in non-treatment seeking nicotine users by our group 
demonstrated that a single session of frontal pole/vmPFC cTBS led to a significant decrease in evoked 
BOLD signal in the orbital prefrontal cortex and the ventral striatum (Figure 3, (63)). All participants 
received real cTBS on one visit and sham cTBS on another visit (randomized) with functional MRI data 
collected immediately before and after the cTBS stimulation (given that single session effects likely abate 
after 1 hr). These data have guided this revised R01 proposal design. 
Other areas of innovation which have been mentioned include: the conceptual focus on developing a tool 
to target the first week after a quit attempt which could ultimately be used in conjunction with a 
comprehensive pharmacotherapy and cognitive behavioral therapy program (Significance 1), and the use 
of an established conceptual model (CNDS theory) to parametrically explore 2 promising treatment 
interventions.  
 
Objectives 
Cigarette smoking constitutes the greatest preventable cause of mortality and morbidity in the US. The most 
critical period for long term success of smoking cessation appears to be in the first 7 days after the quit date. 
A metaanalysis of 3 pharmacotherapy trials revealed that abstinence during the first 7 days was the strongest 
predictor of 6 month outcomes (n=1649; Odds ratio: 1.4, P <0.0001; Ashare et al. 2013). Prodigious relapse 
rates during this first week of smoking cessation are likely due to behavioral and neurobiological factors 
that contribute to high cue-associated craving and low executive control over smoking. The long term goal 
of the research is to develop evidence-based transcranial magnetic stimulation (TMS) protocols to 
facilitate abstinence during this critical period.  
The competing neurobehavioral decision systems (CNDS) theory posits that in addiction, choice results 
from a regulatory imbalance between two decision systems (impulsive and executive). These behavioral 
systems are functionally linked to two discrete frontal-striatal circuits which regulate limbic and executive 
control (2). Modulating these competing neural circuits (e.g. either dampening the limbic/impulsive 
system or amplifying the executive control system), may render smokers less vulnerable to factors 
associated with relapse. The scientific premise for the proposed research is that direct modulation of these 
neural circuits will induce changes in cigarette valuation and brain reactivity to smoking cues. However, 
the relative efficacy of targeting one or the other systems is unknown. To address this gap we will target 
the two components derived from the CNDS.  
These two frontal-striatal neural circuits - the limbic loop (ventromedial prefrontal cortex (vmPFC)-ventral 
striatum), and executive control loop (dorsolateral PFC (dlPFC)-dorsal striatum) can be differentially 
stimulated by theta burst stimulation (TBS), a patterned form of transcranial magnetic stimulation. 
Continuous TBS (cTBS) results in long term depression (LTD) of cortical excitability and intermittent TBS 
(iTBS) results in potentiation (LTP) (3). Recent studies by our group have demonstrated that LTD-like 
cTBS to the vmPFC (Aim 1) attenuates brain activity in the nucleus accumbens (Hanlon et al. 2015) and 
salience network (2017). In a collaborative MUSC/VTCRI study, 5 days of vmPFC cTBS reduced the value 
of cigarettes, preference for immediate gratification, and smoking cue-evoked brain activity (see 
Preliminary data). Alternatively, other groups have demonstrated that LTP-like stimulation to the dlPFC 
(Aim 2) decreases cigarette craving and cigarette use (4,5). These studies support the targets specified by 
CNDS. We will evaluate the relative efficacy of these 2 strategies as novel tools to change smoking-related 
behaviors and dampen brain reactivity to cues in two double-blind, sham-controlled neuroimaging studies. 
Our long-term vision is that TBS would be used as an acute intervention enabling individuals to get through 

Protocol version: 4.4 
Protocol date: 06/10/2022 
7 
the first week after a smoking quit attempt without relapsing, and transition to more sustainable mechanisms 
of behavioral change (e.g., medication, cognitive behavioral therapy).   
Aim 1 (Strategy 1): Modulating the limbic system as an approach to treatment: mPFC iTBS. Cigarette 
smokers will be randomized to receive 10 days of real iTBS or sham iTBS directed to the mPFC. 
Intermittently the desire to smoke, cigarette value using behavioral economic demand, preference for 
immediate gratification (delay discounting), and cigarette self-administration will be assessed. Smoking 
cue-evoked brain activity will also be measured when individuals are asked to ‘crave’ (passive limbic 
engagement) versus ‘resist’ the craving (executive engagement)(7,11-13). We hypothesize that iTBS will: 
1) decrease the behavioral smoking measures described above, which will be explained by a selective 2) 
decrease in the neural response to cues when individuals ‘allow’ themselves to crave, and 3) sustain these 
changes over a time period sufficient to overcome the initial quit attempt (~7-14 days).  
Aim 2 (Strategy 2): Modulating the executive system as an approach to treatment: dlPFC iTBS. Aim 
2 will follow the design of Aim 1. The procedures will be identical, except iTBS will be delivered to the 
left dlPFC. We hypothesize that iTBS will: 1) decrease the behavioral smoking measures described above, 
which will be explained by a selective 2) increase in the neural response to cues when individuals attempt 
to ‘resist’ the cues, and again 3) sustain these changes over a similar period as specified in Aim 1.  
Exploratory Aim-  Evaluate baseline frontal striatal connectivity and discounting rate as factors to 
predict an individual's likelihood of responding to Strategy 1 versus Strategy 2. We will test the 
hypotheses that individuals with a higher ratio of (mPFC-striatal)/(dlPFC-striatal) connectivity will be more 
likely to have a behavioral change after Strategy 1. Various demographics (e.g. gender, smoking history, 
socioeconomic status, subclinical depressive symptoms, self-efficacy, & motivation to quit will be 
evaluated as explanatory variables. 
The outcomes of the present aims will resolve a critical gap in our knowledge regarding the relative efficacy 
of 2 promising TMS treatment strategies.   These outcomes will be directly translated to a larger longitudinal 
study evaluating a multipronged approach to improving outcomes in traditional pharmacotherapy or 
behavioral treatments.  
 
Methods and Measures 
Design 
The long term goal of the research is to develop non-invasive brain stimulation as an evidence-based tool 
to facilitate abstinence during the first week after a quit attempt, as this is a robust predictor of 6-month 
treatment outcomes with traditional pharmacotherapeutic and behavioral interventions. The next step in 
pursuit of that goal is to systematically and parametrically evaluate the efficacy of two promising TMS 
treatment strategies (LTP-like stimulation to the mPFC (Aim 1), LTP-like stimulation to the dlPFC (Aim 
2)) as tools to change smoking behavior and brain reactivity to smoking cues. These aims will be addressed 
in serial (Aim 1 Years 1-3, Aim 2 Years 3-5). A randomized, double-blind, sham-controlled design will be 
used for each aim. In total, 138 smokers will be recruited from the tri-county area surrounding Winston-
Salem, NC.  Current cigarette smokers seeking to quit smoking and willing to set a quit date will be recruited 
from the tri-county area using digital and print advertising in diverse media outlets. Following informed 
consent and screening, participants will be randomized to receive real or sham TMS to the mPFC (Aim 1, 
Years 1-3) or the dlPFC (Aim 2, Years 3-5) (see Table 1 and TMS Procedures below for more details 
regarding sham controls and double blinding procedure). During the Treatment Phase, participants will 
receive 10 sequential days of TMS (with additional behavioral and MRI scanning (see Table 1). Participants 
will receive TMS at least 3 times a week for a total of 10 TMS sessions. During the follow-up phase, 
participants will receive 4 maintenance TMS sessions once a week for 4 weeks. During the Follow Up 
Phase, behavioral assessments will be acquired weekly for the first 4 weeks (see Table 2), and daily EMA 
will be acquired.  Consistent with the CNDS model, the primary hypotheses are that LTP-like 
stimulation to the mPFC and LTP-like stimulation to the dlPFC will both lead to a decrease in 

Protocol version: 4.4 
Protocol date: 06/10/2022 
8 
smoking cue-reactivity in the salience network (neuroimaging outcome), and cigarette value 
(behavioral outcome) but that directly targeting the value circuit (mPFC) will be more efficacious.   
Additionally, we will test the hypothesis that individuals with higher discounting rates and cigarette 
valuation at baseline will respond more to the stimulation strategies compared to those with lower 
baseline discounting and valuation (Exploratory Aim).   
 
 
Setting 
All study activities will take place at Wake Forest University of Health Sciences (WFUHS). 
 
Dr. Hanlon’s primary office and research laboratory is located in the Clinical Neuromodulation 
Laboratory in the Department of Cancer Biology. Dr. Hanlon’s lab space will include a room dedicated 
for all research related activities including a space for screening participants and a space dedicated for 
TMS stimulation. It will contain a computer and desk for participant interviewing and a TMS system. 
 
The MRI portion of the study will take place at the MRI center located on Medical Center Boulevard. 
This will utilize the Siemens 3T scanner in the MRI center. 
 
Finally, recruitment efforts will come from the local community using flyers as well as traditional and 
social media outlets (radio, television, Facebook, Craigslist, local newspapers). Collaborative efforts will 
be maximized in order to recruit subjects from the associated Wake Forest University smoking programs. 
 
Subjects selection criteria 
We anticipate that we will be able to recruit 138 eligible individuals from the communities surrounding 
WFUHS. We anticipate that 56% of subjects will be male and 44% female, and that approximately 14% of 
subjects will be racial and ethnic minorities. The risks of MRI and TMS to the unborn fetus are not well 
understood. Therefore, to be included, females must not be pregnant as determined by a urine pregnancy 
test and must be utilizing reliable birth control during the course of the study. 
 
Inclusion Criteria 
1. 
Age 18-75 (to maximize participation, and minimize effects of cortical atrophy on neuroimaging 
data)  
2. 
Current cigarette smoker (at least 10 cigarettes per day) 
3. 
Able to read and understand questionnaires and informed consent. 
4. 
Has accommodations within 50 miles of the study site. 
5. 
Is not at elevated risk of seizure (i.e., does not have a history of seizures, is not currently prescribed 
medications known to lower seizure threshold)  

Protocol version: 4.4 
Protocol date: 06/10/2022 
9 
6. 
Does not have metal objects in the head/neck.  
7. 
Does not have a history of traumatic brain injury, including a head injury that resulted in 
hospitalization, loss of consciousness for more than 10 minutes, or having ever been informed that they 
have an epidural, subdural, or subarachnoid hemorrhage.  
8. 
Does not have a history of claustrophobia leading to significant clinical anxiety symptoms. 
 
Exclusion Criteria 
1. 
Any psychoactive illicit substance use (except marijuana, alcohol, and nicotine) within the last 30 
days by self-report and urine drug screen. For marijuana, no use within the last seven days by verbal report 
and negative (or decreasing) urine THC levels. Participation will be discontinued if participants use 
psychoactive illicit substances (except nicotine and alcohol) after study initiation.  
2. 
Meets DSM-V criteria for current axis I disorders of major depression, panic disorder, obsessive-
compulsive disorder, post-traumatic stress syndrome, bipolar affective disorder, schizophrenia, dissociate 
disorders, eating disorders, and any other psychotic disorder or organic mental disorder.   
3. 
Has current suicidal ideation or homicidal ideation. 
4. 
Has the need for acute treatment with any psychoactive medication including anti-seizure 
medications and medications for ADHD. 
5. 
Females of childbearing potential who are pregnant (by urine HCG), nursing, or who are not using 
a reliable form of birth control. 
6. 
Has current charges pending for a violent crime (not including DUI related offenses). 
7. 
Does not have a stable living situation. 
8. 
Suffers from chronic migraines. 
9. 
Does not have a stable phone number for contact through calling and/or texting. 
10. 
Does not have a stable means of using WebEx (e.g. personal computer, Internet) for interaction 
with study personnel during COVID-19. 
 
Sample Size 
Power. Aims 1& 2 are powered to detect a quantifiable and clinically meaningful difference in several 
primary outcome measures (neuroimaging and behavioral). The hypothesis for the sample size calculations 
corresponded to the treatment phase for the behavioral outcomes. Based on data from our preliminary 
study of cTBS to the vmPFC (see Significance) and our previously published study on dlPFC TMS a 
sample size of 28 individuals per group will yield at 80% power will be adequate to detect a minimum 
effect size d=0.8. For all sample size calculations, we used a repeated measures design with 4 time points 
using the observed AR (1) correlations. Aim 2 will be evaluated in a manner identical to Aim 1.  Retention. 
Based on published data (5) and prior studies in this population (NIH R21 PI: Li), we anticipate an overall 
retention rate of 78% for the treatment phase, wherein the dropout rate declines as the study progresses 
(retention: first week (0.80 V1-V6), 2nd week (0.85 V6-V10; (0.89 V11-4wk follow up). Allowing for a 
10% data loss associated with excessive movement during neuroimaging, this recruitment protocol will 
lead to a final sample size of 36 individuals that received mPFC TMS (Aim 1), dlPFC TMS (Aim 2), and 
Sham TMS to each site (Aim 1&2).  We do not expect a difference in attrition between the real and sham 
groups, nor the mPFC (Aim 1) versus dlPFC (Aim 2) location. Thus an initial sample of 138 individuals, 
randomized to 4 groups, will yield a final sample of 108 usable data sets at V11 providing 80% power 
with a type 1 error rate of 5% to detect a minimum effect size of d=0.8 in the Behavioral and Brain 
Measures.  Primary data analysis will be done on the Intent to Treat sample (n=138; 69 per Aim: 46 
active, 23 sham).   
 
In our experience retention rates do not differ during 10 days of real or sham TMS, nor mPFC versus 
dlPFC stimulation, based on ongoing studies in Dr. Hanlon’s laboratory and Dr. Li’s laboratory in which 
individuals with cocaine, alcohol, and/or nicotine dependence are receiving 10 days of TMS (R21 
(Hanlon); (R21 (Li)). All individuals will be enrolled at WFUHS. 

Protocol version: 4.4 
Protocol date: 06/10/2022 
10 
 
Due to several COVID related factors, including challenges with scheduling and some hesitation 
participants may have wearing a mask in the MRI scanner, some individuals will not receive the MRI 
portion of this experiment.  This will neither affect our total enrollment goals, nor compromise the 
scientific integrity of the study.  Moreover, it will lower the risk associated with MRIs to these 
participants. 
 
Interventions and Interactions 
Participants and Procedure. A total of 138 non-treatment-seeking nicotine-dependent men and women, 
18-75 years old will be recruited from the local community using flyers, as well as traditional and social 
media outlets (radio, television, Facebook, Craigslist, local newspapers). Following initial contact, and 
informed consent, participants will be scheduled for a screening visit at WFUHS (inclusion/exclusion 
criteria described above). Participants must be motivated to quit and willing to make a quit attempt with the 
use of TMS and educational booklets for smoking alone (“Forever Free – A Guide To Remaining Smoke 
Free” Tobacco Research and Intervention Program).  {Note: In the interest of experimental rigor, in this 
study we wanted to directly evaluate the efficacy of these 2 TMS strategies of smoking cessation without 
the confound of pharmacologic manipulation. Therefore, participants that would prefer to use 
pharmacotherapy immediately after the quit date will be referred to the WFUHS Smoking Cessation 
Treatment program and will not be eligible to enroll. Following the 4 week follow up, interested participants 
will be referred to a WFUHS Smoking Cessation Treatment program wherein they will have the opportunity 
to receive additional pharmacotherapies for smoking cessation}.  Eligible participants will work with the 
clinical research team to set a quit date (within 2 weeks of the screening visit). They will be encouraged to 
set “Monday” as their quit date given prior studies that have shown higher motivation to quit on Mondays 
(120). They will be introduced to the Forever Free booklets at the first TMS visit. One aspect of these 
educational booklets will be discussed with the participants on each TMS visit. Although this program has 
low to moderate efficacy on its own (especially in the first 7 days), it is unlikely that TMS alone will be 
sufficient for smoking cessation. Therefore, all participants will also be exposed to these booklets by the 
Tobacco Research and Intervention Program.  
Study Visit 1 (Behavior, MRI, TMS) will begin 1 week prior to the quit date.  Inclusion/Exclusion criteria: 
To participate in the present study, participants must: 1) be 18-75 years of age, (2) smoke at least 10 
cigarettes a day (on average; as measured by 1 month timeline follow-back (TLFB), (3) not have a history 
of or current psychiatric or neurologic disease, or traumatic brain injury (4) not be pregnant, , (5) not 
currently use psychoactive substances other than alcohol, nicotine, or marijuana; nor have current or recent 
(e.g. within the past 5 years) moderate/severe substance use disorder (DSM-V) (6) not have any barriers to 
making contact between the TMS coil and the skin (e.g. braids that cannot be removed), and 7) meet all 
criteria on a standardized MRI/TMS safety screen (TASS) (including but not limited to implanted electronic 
devices, bullets or metallic fragments, hair clips and piercings that cannot be removed). These 
inclusion/exclusion criteria are those used in Study 4 described in Preliminary Research. Randomization. 
Participants will be individually randomized and assigned to treatment or control conditions using stratified 
block randomization (with blocks of randomly varying sizes) prior to the study.  
Experimental Procedure (Aim 1 and 2): Screening Visit – Consent. Participants will receive a series of 
assessments designed to evaluate nicotine dependence and use, psychiatric conditions, and mood. These 
include a standard clinical intake evaluation screen for research (used in other studies by the PI), MINI 
International Neuropsychiatric Interview (MINI 5 P) (88), Timeline Follow-Back (TLFB) (89), Beck 
Depression Inventory II (BDI) (90), State-Trait Anxiety Inventory (STAI) (91) and Profile of Mood States 
(POMS) (92). Data will be collected using REDCap™, and entered directly into the online portal to ensure 
security and prevent data loss. 

Protocol version: 4.4 
Protocol date: 06/10/2022 
11 
During COVID-19, study personnel will interact with participants via the Wake Forest Baptist Health 
(WFBH) institutional WebEx videoconference software as necessary.  Participants will remotely sign and 
date the informed consent document. 
Study Visit Procedures: At all visits, individuals will provide a urine screen to detect recent use of drugs 
of abuse and pregnancy, a breath carbon monoxide (CO) sample to detect recent cigarette smoking, and a 
breath sample to test for recent alcohol use. During the consent visit, visits 1, 6, 10 and follow up visits 
samples will be collected to determine cotinine (nicotine metabolites) levels in the urine. Samples will be 
taken from the urine sample provided for the drugs of abuse screen, no additional samples will be required 
from the participant. At visits 1, 6, 10 and follow up visits (Assessment visits), participants will complete 
a battery of behavioral assessments following active TMS or sham (see Table 2). MRI scanning (at visits 1 
and 10) will occur prior to TMS in order to isolate cumulative, rather than acute, effects of TMS. At all 
visits participants will be required to provide a CO reading at least 50% of their baseline CO measure to 
ensure they are at least 6 hours deprived from cigarettes. Cigarette deprivation will be use to increase 
craving and responding for cigarettes. Short term cigarette deprivation has known amplification effects on 
the brain response to smoking cues (93, 94). Moreover, we have extensive experience with successfully 
using this deprivation procedure (33, 41). 

Protocol version: 4.4 
Protocol date: 06/10/2022 
12 
1. Brain Reactivity to Smoking Cues (**PRIMARY DEPENDENT MEASURE) 
Functional MRI 
Smoking Cue 
Reactivity Task  
(acquired after 
behavioral 
assessments) 
15 
1, 10 
This task has been used in 4 original research publications by our group 
and reliably activates orbitofrontal and medial prefrontal regions (7, 11-
13). We will give participants an unlit cigarette to hold in their hand while 
they are in the MRI scanner. They will receive two 12m runs of the task 
(fixed within subject, counterbalanced within groups). During one run 
they will be instructed to ‘allow’ themselves to crave (passive limbic 
engagement) and in the other run they will be told to ‘resist’ the cues 
(executive engagement). When the task begins participants are shown 
pseudorandomly interspersed blocks of images: 1) cigarette images (e.g., 
cigarettes, lighters, people smoking) (CIG), 2) neutral images, 
Table 2 
1. Biochemical and self-report assessment of tobacco use and dependence 
Measure 
Time 
(Min.) Visits 
Description 
Carbon 
monoxide (CO 
Level) 
1 
1-10, and 
follow-ups 
Breath CO provides an accurate measure of recent exposure to CO, 
including from smoking combustible tobacco products (95). The CO 
measurement will be used to determine recency of smoking. 
Timeline 
Follow-Back 
(TLFB) 
interview 
5 
1, 6, 10, 
and 
follow-ups 
This self-reported tobacco product use interview asks participants to 
retrospectively estimate the number of tobacco products they’ve used 
each day for the past 30 days or since the previous assessment, 
whichever is fewer (89). 
Fagerström 
(FTND) 
3 
1, 6, 10, 
and 
follow-ups 
This 6-item questionnaire quickly assesses degree of cigarette 
dependence (96, 97). Test for Cigarette Dependence. 
2. Measures of tobacco product value and sensitivity (**PRIMARY DEPENDENT MEASURES) 
Questionnaire 
on Smoking 
Urges-Brief 
(QSU) 
 
2 
1 - 10 and 
follow-ups 
This 10-item questionnaire assesses cigarette craving. Participants will 
be asked to rate from 1 (strongly disagree) to 100 (strongly agree) their 
agreement with several statements (e.g., "I have a desire for a cigarette 
right now," and "Nothing would be better than smoking a cigarette right 
now."). Factor analyses support two factors: one that reflects a strong 
desire and intention to smoke and one that reflects relief from negative 
affect associated with an urgent desire to smoke (45). 
Hypothetical 
Purchase Task 
(**Demand 
Alpha, Q0) 
(HPT 
Cigarettes) 
5 
1, 6, 10, 
and 
follow-ups 
The hypothetical purchase task (HPT), a validated measure for cigarette 
demand, (98, 99) will assess cigarette purchases at various price 
conditions. Participants will hypothetically purchase quantities of 
cigarettes to use over a 24-hour period across ascending prices (e.g., $0, 
0.12, $0.25, $0.50, $1.00, and $2.00 per cigarette). The HPT has been 
shown to be predictive of treatment outcomes (100). 
3. Cognitive/behavioral task (** PRIMARY DEPENDENT MEASURE) 
Delay-
discounting 
tasks  
(*discounting 
rate, $1K) 
5 
1, 6, 10, 
and 
follow-ups 
Participants will be asked to choose between two conditions in which 
varying amounts and delays to behavioral outcomes (e.g., $50 now or 
$100 later) are presented. We will use magnitudes of $100 and $1000 
which are the most thoroughly documented among the literature (101). 
Across consecutive choices, the delay to the larger outcome will be 
titrated until reaching the participant’s indifference delay (i.e., the delay 
at which s/he equally values both options). This indifference delay 
indexes individual participants’ rates of delay discounting. [61]. 
Monetary delay discounting has been documented to be predictive to 
treatment success for smokers (39). 

Protocol version: 4.4 
Protocol date: 06/10/2022 
13 
chromatically matched to the CIG images (NEU), and 3) blurry non-
images (BLUR). Stimuli are presented in six 120-s epochs, each 
consisting of four 24-s blocks of an image type (one block each of CIG, 
NEU, BLUR). Each block is followed by a 6-s washout period, allowing 
the hemodynamic response from the previous block to decline before the 
next is presented. Scanning parameters: Multislice gradient-echo echo 
planar imaging (TR=2200, TE=35 msec, field of view of 192 mm, voxel 
size 3.0 x 3.0 x 3.0 mm, 36 slices, 3 mm thickness, no skip). 
2. Other Assessments 
Confidence in 
Active or 
Sham TMS 
Assignment 
(Active vs 
Sham) 
1 
1, 6, 10 
Participants will complete a form indicating their confidence (scale 1-10) 
in whether they are receiving active or sham treatment. Pooled accuracy 
from prior work in our collaborator’s laboratory was 47.6% suggesting that 
individuals were not aware of the stimulation being received. 
Minnesota 
Nicotine 
Withdrawal 
Scale (MNWS) 
2 
1 - 10 and 
follow-ups 
Participants will be asked to rate a variety of mood and physiological 
symptoms associated with nicotine withdrawal (e.g., anxiety, attention, 
hunger). Total withdrawal scores will be measured using the 8-item MNWS 
originally adapted from Hughes and Hatsukami 1986 (102) and Hughes 
1992 (103). The 8-item scale is a validated measure and the most frequently 
used (104, 105). 
Beck Depression 
Inventory (BDI) 
 
10 
1,6,10,and 
follow-ups 
Given that iTBS to the left dlPFC improves depression scores, we will 
perform a mediation analysis to determine if any effects observed on 
smoking behavior and the brain response to smoking cues is related to 
changes in depression  
Generalized 
Self-efficacy 
scale (GSF) 
10 
1,6,10,and 
follow ups 
This scale was designed to assess self-efficacy, i.e., the belief that one’s 
actions are responsible for successful outcomes. The scaled score for each 
question ranges from 1 to 4. Higher scores indicate stronger participant’s 
belief in self-efficacy. Previous studies have demonstrated predictive 
validity of self-efficacy as baseline predictor of smoking treatment 
outcome (112). 
3. Other neuroimaging sequences 
T1 MPRAGE 
5 
1,10 
High-resolution structural scans will be obtained using an inversion 
recovery 3D spoiled gradient echo (3DSPGR) sequence, 192 slices, voxel 
size: 1.0 x 1.0 x 1.0, field of view: 256 mm, section thickness:1.0 mm with 
no gap, giving an in-plane resolution of 256. This sequence will be used for 
anatomic overlays of the functional data and spatial normalization to a 
standard atlas, TMS coil positioning, and subsequent voxel-based 
morphometry.  
Field Map 
1 
1,10 
Field Map calculations allow for offline correction of imaging distortion 
during post-processing. Field map configurations will include: flip angle 
(FA)= 60 degrees, field of view (FOV) = 192 mm; voxel size = 3.0 x 3.0 x 
3.0 mm, 36 contiguous slices 3.0 mm thick.  
 
 
 
 

Protocol version: 4.4 
Protocol date: 06/10/2022 
14 
TMS Treatment – 10 days (Experiment 1/Strategy 1: iTBS to the mPFC; Experiment 2/Strategy 2: 
iTBS to the dlPFC) 
After the MRI scanning visits participants will be escorted into the Brain Stimulation Research Laboratory 
(Dr. Hanlon’s research suite) where scalp localization will be performed for the TMS procedure. The 
Cartesian position of the coil (X,Y,Z) will be determined by standardized positions from the EEG 10-20 
system: 1)  AFZ will be used for the mPFC stimulation (Aim 1), 2) F3 will be used for the left dlPFC 
stimulation (Aim 2). The angular position of the coil (pitch, yaw, roll) will be determined by the individual’s 
cortical geography using the individual’s T1 scan for guidance. The locations and coil orientation will be 
indicated on a nylon cap which will be worn during the TMS sessions. We will then determine the 
participant’s resting motor threshold (RMT, the minimal amount of stimulation required over the hand area 
of the primary motor cortex to induce contraction of the APB muscle of the hand 50% of the time) via the 
standardized PEST procedure (106, 107). The procedures for acquiring the motor threshold, performing 
cortical localization, standardized procedures, blinding, establishing standardized paradigms and training 
regimens for all staff, as well as safety and ring the experimental procedures are consistent with our prior 
publications (7,27, 54, 63, 69, 78, 111). We will also publish a Standard Operating Procedure document 
and video file as supplementary material with any publications that arise from this project (as in 111, 
supplement).  
For Aim1 (Strategy 1) stimulation will be over mPFC. For Aim 2 (Strategy 2) stimulation will be over the 
left dlPFC. The Aims will be pursued in serial, with a 2:1 active versus sham randomization for each Aim. 
Participants will receive 20 trains of stimulation (1200 pulses total; each train: 3 pulse bursts presented at 
5Hz, 15 pulses/sec for 2 sec, 8 sec rest, 10 pulses/train; 110% RMT, MagPro) using a figure 8 coil (Coil 
Cool-B65 A/P). This is the iTBS sequence initially published (3) which has been used in clinical depression 
treatment (46). During each real and sham TBS session each day the amplifier output will be escalated 
(“ramping” in 5% increments over 30 seconds) up to  80% to 110% RMT to enhance tolerability. The time 
between the end of the TBS procedures and the beginning of the behavioral assessments will be compiled 
and used as covariates in subsequent analyses.  
TMS ACTIVE SHAM system and strategies to promote rigor of the blind: : The MagVenture MagPro 
system has an integrated active sham that used two surface electrodes placed on the scalp to mimic the 
sensation of active TMS.  The coil is visually identical on both sides, but only one side will direct an electric 
field on the participants head. At each TMS visit, the coordinator will place 2 surface electrodes on the 
scalp near the TMS stimulation site. Once the electrodes are in place and the TMS coil is in position, the 
coordinator enters a 6 digit number assigned to that participant into the MagPro capacitor.   Each number 
is coded to be either active or sham TMS, but the classification is unknown to the coordinator or the 
participant.  The capacitor can sense whether the TMS coil is positioned in a direction wherein the active 
or the sham side is facing the participant’s head. The capacitor will instruct the coordinator that is either 
“Ready” or to “Flip Coil” based upon the participants group assignment.   This enables both the participant 
and the coordinator to be blind.    
For the initial TMS visit, during the motor threshold procedure, the active side is always down in order to 
determine an appropriate dose   To ensure that the coordinator (who both finds the motor threshold and then 
delivers the treatment) does not learn the relationship, on Visit 1 after the motor threshold procedure is 
done, the coordinator will leave the room and a second individual will unplug the coil, flip it 1 or more 
times, and  leave the room.  The coordinator will return to the room, plug it in, place the TMS coil in the 
proper position, and commence the treatment.   
Further strategies to promote blinding: The TMS coil will be visually inspected by the PI or lab manager 
on a regular basis to be sure that no marks have occurred on the surface of the coil.  If marks have occurred 
they will be either washed off or covered in a manner that is symmetrical to both sides of the coil. 
Additionally all staff will be trained on the importance of blinding when they join the laboratory and 
encouraged to contact the lab manager or the PI if they ever feel they may have “figured out” the blind. A 

Protocol version: 4.4 
Protocol date: 06/10/2022 
15 
questionnaire will be given to the participants to evaluate their opinion on whether they received active or 
sham, their level of confidence (Likert scale 1-10), and their rationale (text entry).   
Outcome Measure(s) 
Behavioral Outcomes: For the treatment phase (10 days of real/sham TBS), the primary behavioral 
outcomes include Cigarette demand (Q0 and alpha), Craving (QSU Intention, QSU Relief), and Delay 
Discounting ($100 and $1000). For the treatment phase the analysis will be performed using repeated 
measures ANOVA on change scores from baseline for each visit. The main independent variable in the 
ANOVA will be time (visits on day 1, 6, 10), group (real or sham) and their interaction. For correlations of 
observations across visits an AR(1) structure will be considered and compared with other structures such 
as compound symmetry and a general structure. The primary hypothesis is the interaction. If the interaction 
effect for an outcome is significant, we will conclude there is statistical evidence of overall treatment effect. 
If the spaghetti plots suggest trends (e.g., linear, quadratic, or piecewise changes) we will treat time as a 
continuous variable and fit repeated measures regression models along with group and its interaction with 
continuous time as independent variables. 
Neuroimaging Outcomes: For the treatment phase, primary analysis. Image pre-processing will be 
conducted with FSL v. 5.0: (FMRIB Analysis Group, Oxford, UK) (108) realignment to the first volume, 
smoothing with an anisotropic 8-mm Gaussian kernel, high-pass filtering, resampling to 2-mm isotropic 
voxels, and stereotactic registration to the Montreal Neurological Institute 152-subject average template. 
Subjects with >3 mm movement (total mean frame displacement across the entire task) will be excluded 
from analysis.  
 
Hypothesis Aim 1: Based on the framework predicted by CNDS theory, and preliminary work (see Study 
4), we anticipate that mPFC stimulation will lead to a decrease in brain reactivity to cues during the “allow” 
condition (passive limbic engagement). We will analyze the neuroimaging data in concert with the 
behavioral data, evaluating the hypotheses that 1) mPFC iTBS will decrease the brain reactivity to cues in 
the ‘allow’ condition (with no consistent link to the ‘resist’ condition) and that 2) this will be related to the 
behavioral change in a dose-dependent manner. This will be achieved by extracting the average BOLD 
signal timecourse and the percent signal change during the CIG versus NEU blocks from a priori 
anatomically defined regions of interest (ROIs), including the striatum (ventral striatum and dorsal 
striatum), insula, inferior frontal gyrus, and anterior cingulate cortex. Ventral striatal and dorsal striatal 
ROIs are derived from the FSL Oxford-GSK-Imanova striatal atlas, mapped via probabilistic diffusion 
tractography (109) in agreement with anatomical projection zones from the frontal cortex (1). Hypothesis 
Aim 2: Based on the framework predicted by CNDS theory, and prior studies of dlPFC stimulation in 
smokers (4-9), we anticipate that dlPFC stimulation will lead to an increase in executive control circuitry 
(left dlPFC, posterior parietal cortex) during the “resist” condition (executive engagement). As in Aim 1, 
we will analyze the neuroimaging data in concert with the behavioral data, evaluating the hypotheses that 
1) dlPFC iTBS will decrease the brain reactivity to cues in the “resist” condition (with no consistent link to 
the “allow” condition) and that 2) this will be related to the behavioral change in a dose-dependent manner. 
The ROI based outcomes of Aim 2 will be compared with the outcomes of Aim 1 using standard 
general linear modeling as well as a data-driven hierarchical model (see Integration below). As an 
experimental control, for both Aims we will also calculate the percent signal change from the primary motor 
cortex, a region which should not be differentially modulated by mPFC or dlPFC stimulation. Although 
some studies have used the visual cortex as a control region a recent meta-analysis by our group 
demonstrated that the visual cortex is typically activated more to drug cues than neutral cues (78). 
Secondary functional connectivity analysis: Additionally, an exploratory seed-based functional 
connectivity analysis using psychophysiological interactions (PPIs) will be performed using the TMS 
stimulated ROIs (mPFC and dlPFC) and the striatal target regions (ventral striatum, dorsal striatum- defined 
above) as seed regions. These data will be compiled across participants and compared between the mPFC 
and dlPFC stimulation days. Group analyses will be performed using a random effects model limiting 
significant clusters to those that meet p<0.05 family-wise error corrected.       

Protocol version: 4.4 
Protocol date: 06/10/2022 
16 
For the follow-up phase: We will test the hypotheses that behavioral change related to Strategy 1 and 2 
will be based on an individual’s baseline brain cue reactivity data. We will generate Kaplan-Meier curves 
of behavioral change for the four groups and compare these with cue-reactivity in the MPFC, insula, 
cingulate (salience network nodes) and striatum using log-rank tests and consider Cox’s proportional 
hazards model to include covariates. Diagnostics will be performed to verify the assumptions such as 
homoscedasticity and normality. If it is merited, transformations on the outcomes will be considered. Linear 
contrasts will be utilized to test the hypothesis of equal effects in the two groups at any given time point. 
Covariates, such as gender and depression that were used to stratify, will be included.  
Exploratory Aim: Evaluate baseline frontal striatal connectivity and discounting rate as factors to 
predict an individual's likelihood of responding to Strategy 1 versus Strategy 2.  Finally, will compare 
the relative efficacy of Strategy 1 versus Strategy 2 on the behavioral change by constructing a 
computational model. We will test the hypotheses that individuals with a higher ratio of (mPFC-
striatal)/(dlPFC-striatal) connectivity will be more likely to have a behavioral change after Strategy 1. 
Various demographics (e.g. gender, smoking history, socioeconomic status, subclinical depressive 
symptoms, self-efficacy, & motivation to quit will be evaluated as explanatory variables.  This will be 
achieved by hierarchical linear modeling (HLM v. 7.0, Scientific Software International, Skokie, IL) of the 
behavioral variables of interest as well as the BOLD signal change in the striatum (caudate) between 
smoking and image blocks (e.g., CIG vs. NEU) in ‘allow’ and ‘resist’ (behavioral change variables nested 
within treatment (cTBS, iTBS)).  
 
Analytical Plan 
Primary data analysis will be done on the Intent to Treat sample (n=138; 69 per Aim: 46 active, 23 
sham).   Prior to formal statistical analysis, summary statistics for all variables will be obtained and 
spaghetti plots will be generated. All behavioral outcome measures will be based on standardized 
composite scores from the literature. We will also use data reduction techniques (such as factor analysis 
or principle component analysis) to confirm the applicability of the composite scores in our population. 
Analyses will be performed for each phase separately. 
Results will be analyzed initially using descriptive statistics.  Comparison between groups will be done 
using chi square tests for proportions, and t-tests or ANOVA procedures for continuous variables.  
Regression analysis will be performed to identify independent outcome predictors.  Other inferential 
statistical analysis will be conducted as appropriate. 
 
Human Subjects Protection 
Potential Risks  
The risks fall into three categories: risks associated with psychological assessment, risks associated with 
repetitive TMS and risks associated with MRI scanning.  
 
Risks of psychiatric interviewing (minimal risk):  
1. Some participants may get emotionally distraught when disclosing sensitive personal stories. Some 
participants may feel anxiety about disclosing substance use histories and reporting some aspects 
of their demographics. 
Risks associated with MRI scanning (minimal risk): 
1. The major potential risks for MRI are all subsumed under the risks for TMS and primarily include 
risks to individuals who have metallic implants, pacemakers, or pregnant women.  These 
individuals will be excluded from the study.   
2. Participants may feel restless or uncomfortable when lying in the MRI scanner.  
 
Risks associated with repetitive TMS (FDA-designated minimal risk): 

Protocol version: 4.4 
Protocol date: 06/10/2022 
17 
Repetitive TMS has been considered “non-significant risk” by the FDA  (2007) when applied at similar 
intensities, durations, and frequencies to those being used in this protocol. Additionally medial prefrontal 
and dorsolateral prefrontal continuous theta burst stimulation in a manner identical to this protocol has been 
designated minimal risk by the MUSC Institutional Review Board for healthy adults as well as individuals 
with nicotine deopendence.   
 
1. 
Potential risk of a seizure: In designing this experiment, we have followed the latest safety 
guidelines for TMS. Despite these precautions, there is a chance of a seizure as a result of rTMS. 
Eight seizures have been noted in previous studies, with six of them occurring in healthy volunteers 
without any history of seizures, brain tumors or traumatic brain injuries. All of these seizures have 
occurred during rTMS with the participant in the treatment chair and a trained operator on hand. 
All seizures have stopped by themselves without any medication. No participants have had any 
problems after the seizures. WFUHS has a plan for dealing with fainting and seizures, and every 
TMS researcher involved in providing TMS treatment for this protocol (Key Personnel) will 
have extensive TMS training from the PI on the study as well as a skills test associated with 
collecting an accurate motor threshold (which is one of the largest factors that promotes 
safety). Additionally, if a participant has a seizure an emergency response team will be called. Most 
seizures, including those caused by rTMS, last less than 60 seconds and do not require any 
medication. Participants will be evaluated by a physician associated with the WFUHS Brain 
Stimulation Laboratory following recovery from the seizure. Any participant who has a seizure 
cannot continue with the study.       
 
A note about theta burst stimulation: The relative risk of having a seizure is related to the strength of the 
TMS stimulation (% motor threshold) and the frequency (typically 1Hz-20Hz, or theta).  There are 
published safety tables for fixed frequency rTMS paradigms (eg 1hz, 5 Hz, 10 Hz, 20 Hz).   For individuals 
receiving TMS doses within these ranges and without other risk factors, (medication, significant sleep 
deprivation, etc.), TMS has been deemed a non-significant risk by the FDA.  For some brain stimulation 
protocols (like theta burst), there are no currently published safety tables, but there are at least 6 review 
articles that demonstrate that theta burst is likely minimal risk to non-significant risk. These studies largely 
show that the risks/safety of theta burst protocols are comparable (or perhaps less than) 10Hz or 20 Hz 
rTMS.     
 
Other potential risks:  
   
2. Potential for scalp discomfort and headaches: Some people report mild discomfort when the 
magnetic pulses are applied over the scalp. A small number of people (~5%) report headache 
following rTMS. However, the headaches are temporary and manageable with common over-the-
counter pain remedies.  
3. Potential hearing loss: The TMS coil generates a high-energy click that may cause hearing damage. 
Humans exposed to TMS have shown temporary increases in auditory threshold (especially at high 
frequencies) lasting at least 5 minutes and less than 4 hours.  
4. Safety in case of pregnancy: This protocol will exclude pregnant women. The risks of using TMS 
with pregnant women are currently unknown. Please inform the research team if you are pregnant 
or think that you might have become pregnant during the study. A pregnancy test will be performed 
before the experiment begins.  
5. Potential for reflex syncopal event: Syncope is defined as a momentary loss of awareness and 
postural tone. It typically has a rapid onset, short duration, and spontaneous recovery.  Although 
syncopal episodes are very rare with TMS (less than 1%), they typically occur during the motor 

Protocol version: 4.4 
Protocol date: 06/10/2022 
18 
threshold procedure before the rTMS treatment has begun.  Individuals that are sleep deprived and 
have low or unstable blood pressure are at greater risk.   
6. Interaction with electrical or metal implants: Electrically, magnetically or mechanically activated 
implants (such as cardiac pacemakers), as well as clips on blood vessels in the brain may be affected 
by rTMS (as well as MRI) and cause pain or abnormal signal propagation.  Therefore individuals 
that have these implants and devices or suspect that they may have pieces of metal in their eyes, 
head, or body (e.g. bullets, shrapnel, fragments from metallurgy) will be excluded from the study. 
Adequacy of protection against risks 
(a) Recruitment and Informed Consent Identification of Subjects, Recruitment of Subjects and 
Informed Consent Process. Advertisements will be placed in local print and digital media. 
Interested individuals will email, call, or text the research center and will then be contacted via 
telephone and scheduled for screening and Visit 1. Only individuals that have previously given 
permission to be contacted for future research purposes will be called. Informed consent will be 
reviewed with the potential participant by a member of the key personnel on this proposal. The 
consent will be signed by the participant as well as one of the Key Personnel on the proposal. A 
copy of the consent will be given to the subject and the original placed in the research record. 
(b) Security of Participant Information 
For individuals that are enrolled in the study (invited for a screening visit) there will be two 
documents that contain their first and last names:  the informed consent containing the HIPAA 
authorization and a receipt for their compensation kept for tax purposes.  Each of these documents 
will be kept in a separate 3-ring binder.   
 
Each individual enrolled in the study will be assigned a unique participant ID number (starting 
sequentially).  A folder will be created for each of these participants and labeled with their 
Participant ID number. The folder will contain the results of all of the testing for each individual.  
The participants will only be identified by number, not by name, on these documents.  All 
information stored digitally for the enrolled participants will be labeled with the Participant ID 
number. As above all of the participant folders, along with the binders will be stored in a locked 
cabinet in Dr. Hanlon’s research laboratory.   
 
Protection Against Risks 
Risks of psychiatric assessments: 
All psychiatric assessments will be conducted by study personnel who have received formal training in 
clinical interviewing and have worked with substance dependent participants in the past.  
 
Risks associated with MRI and TMS (minimal risk): 
1. Although the TMS protocol that we are using has never been associated with causing a seizure, 
individuals that have a history of seizures, stroke, or other neurological impairment that might 
lower their seizure threshold will be excluded from the study. All study personnel will have 
received a formal education course in seizure detection, care, and treatment and a physician will be 
available to immidiately assist in stabilizing the participants in the event of a seizure  Any 
participant who has a seizure cannot continue with the study. 
2. We will exclude individuals with claustrophobia such that they are not exposed to this risk. 
Additionally participants will be given a pressure sensitive squeeze ball that they can use to indicate 
at any time that they would like to leave the scanner.  
3. To protect against hearing loss concerns, participants will wear high fidelity earplugs throughout 
the scanning session.   

Protocol version: 4.4 
Protocol date: 06/10/2022 
19 
4. Participants will be informed of potential risk of scalp discomfort and headache before they consent 
and will be told that they should feel free to take non-steroidal antiinflammatory agents after the 
TMS session  if they have a headache.We will also exclude individuals with chronic migraines such 
that they are not exposed to this risk.  
5. We will exclude pregnant females such that they are not exposed to this risk.  
6. All participants that enroll in this study will complete a written MRI saftey screen.  We will also 
use a handheld metal detector to ensure the participant has no metal in or on is/her body before 
entering the MRI scanning room.    
 
Participants may withdraw from the study at any time or may be withdrawn from the study if the PIs feel it 
is in the best interest of the participant. All key personnel will undergo appropriate IRB training for dealing 
with human participants and will be trained by the PI at their site in all aspects of the study interventions. 
Personnel listed in this protocol (as well as any rotating medical students, graduate students, psychiatry 
residents or fellows that may be exposed to this investigation as part of their reseaerch training exposre) 
will be required to maintain their certification of HIPAA training and Protection of Human Participants in 
Research training on an annual basis. Any new personnel without experience in human clinical research 
will be encouraged to attend the WFUHS Core Clinical Research Training Course, which is offered live 
and online throughout the year. Through these measures we will ensure that all study staff will be trained 
and will maintain ongoing understanding of research ethics and the rights of the participant during the 
consenting process and throughout an individual's participation in the study. 
 
In the event of a medical emergency, a research participant will be transported to the Emergency 
Department at WFUHS. If a psychiatric crisis occurs, the Department of Psychiatry will be contacted to 
arrange for either an emergency outpatient appointment or an in house psychiatric consult.  
 
Protocol for participants expressing suicidal ideation: All study team members performing the Becks 
Depression Inventory will have received online training from the Suicide Prevention Resource Center 
(https://training.sprc.org). Completion documentation will be saved on the laboratory drive. In the event 
that a participant expresses a desire to kill themselves (selects answer #2 or #3 on question #9 of the Becks 
Depression Inventory), the trained study team member will ask them about the level of detail of their 
thoughts.  If the participant has a suicide plan to kill himself/herself, the study staff will recommend he/she 
speaks with the suicide hotline and initiate contact with the suicide prevention hotline (Durham Center 
Crisis Line at 1-800-510-9132) while the individual is in their presence. If the participant refuses to talk to 
the hotline and leaves, the study staff will call 911. The study staff member will also contact the PI via 
phone, email, or text as soon as possible to inform them of the situation. 
 
Subject Recruitment Methods 
Advertisements will be placed around campus in approved locations, especially at WFUHS clinics. Other 
ads will be submitted to local newspapers as well as internet advertising to reach the general population 
(e.g. Craigslist, broadcast messages at WFUHS). Recruitment will also occur at community events where 
recruitment materials (such as pens, backpacks, and mugs) will be handed out to individuals. Interested 
individuals will email, call, or text the research center and will then be contacted via telephone, phone 
screened, and scheduled for screening if eligible. If an individual declines study participation or is not 
eligible via phone screen, their information will be shredded and destroyed. Informed consent will be 
reviewed with the potential participant by a member of the key personnel on this visit. The consent will 
be signed by the participant as well as one of the Key Personnel on the proposal. A copy of the consent 
will be given to the subject and the original placed in the research record. The consent and HIPAA 
process will be done in Dr. Hanlon’s research laboratory and facility.  The MRI scans will be done at the 

Protocol version: 4.4 
Protocol date: 06/10/2022 
20 
MRI center and the TMS sessions will be done in the TMS laboratory located in Dr. Hanlon’s research 
lab.   
 
Additionally, a chart review will be conducted for research purposes.  Potentially eligible participants will 
be identified.  The potentially eligible participants in the PIs practice will be informed about the study as 
the PI feels is appropriate. Then potential participants who have agreed to be contacted for future research 
by logging their WFU Research Permissions preferences in MyChart will be contacted by phone and invited 
to participate. All other participants will be contacted through their providers to be informed of the study if 
the provider feels it is appropriate. 
 
Informed Consent 
Individuals that have previously consented to be contacted about future research studies will be contacted 
and phone screened to determine preliminary eligibility. They will be scheduled for their screening visit, 
which will take place in a private, quiet screening room in the Clinical Neuromodulation Laboratory 
space in Dr. Hanlon’s research suite. Informed consent will be reviewed with the potential participant by 
a member of the key personnel on this proposal. The consent will be signed by the participant as well as 
one of the Key Personnel on the proposal. A copy of the consent will be given to the subject and the 
original placed in the research record. All records will be stored in locked departmental files. Section 
301(d) of the Public Health Service Act of November 4, 1988 also protects a layer of protection for the 
privacy of health information for individuals that engage in federally funded medical research. 
 
Confidentiality and Privacy 
Confidentiality will be protected by collecting only information needed to assess study outcomes, 
minimizing to the fullest extent possible the collection of any information that could directly identify 
subjects, and maintaining all study information in a secure manner.  To help ensure subject privacy and 
confidentiality, only a unique study identifier will appear on the data collection form.  Any collected 
participant identifying information corresponding to the unique study identifier will be maintained on a 
linkage file, store separately from the data.  The linkage file will be kept secure, with access limited to 
designated study personnel.  Following data collection subject identifying information will be destroyed 
three years after closure of the study, consistent with data validation and study design, producing an 
anonymous analytical data set.  Data access will be limited to study staff.  Data and records will be kept 
locked and secured, with any computer data password protected.  No reference to any individual 
participant will appear in reports, presentations, or publications that may arise from the study. 
 
Data and Safety Monitoring 
The principal investigator (PI) will be the primary party responsible for data management, oversight, and 
accountability in terms of participant safety and consent.  
The DSMB member holing the unblinding key (Elizabeth Shilling) will have the following 
responsibilities:  
1) Storing the unblinding spreadsheet on a secure server that is not accessible by members of the study 
team that are blinded 
2) In the event of a safety event (and at the conclusion of the year), she will have the ability to look up 
the condition the participant was receiving (active/placebo) 
3) A conflict of interest will be avoided by secondary evaluation of records by a Monitoring Entity (ME) 
(aka. data safety monitoring board- DSMB) on an annual basis. Quality control will include regular data 
verification (Integrity of the Consent and HIPAA, scores on assessments, MRI scanning information), 
study progress, subject status, adverse events, and protocol deviations. Protocol adherence will be 
monitored by the Wake Forest IRB, who will also be given access to the reports from the PI to the ME. 
 
Provisions to Monitor the Data to Ensure the Safety of Subjects 

Protocol version: 4.4 
Protocol date: 06/10/2022 
21 
DSM Board Plan: Meet annually with the PI to discuss the information listed in “Content of ME/DSM 
report”. This content of this meeting will be formalized in a report which will be circulated by email and 
digitally approved by the PI and ME/DSMB. The approved report will be sent to the Wake Forest IRB, 
and to NIH. 
 
Content of DSM Report: The following information will be included in the DSM report- number of 
individuals consented, number of individuals enrolled, number of active participants, gender and race 
distribution of subjects, discussion and listing of all amendments to the proposal, any publications and/or 
scientific presentations related to the proposal, update on any resolved or unresolved AE/SAEs, review of 
any new scientific literature related to the safety and efficacy of this protocol.  
 
Plans for Interim Analysis of Efficacy Data: Data from this study will be analyzed when 20% 
increments of the recruitment goal have been obtained(e.g. 20% (n=27), 40% (n=55), 60% (n=83), 80% 
(n=110), 100% of the total enrolled sample (n=138)).  A member of the DSMB that holds a copy of the 
unblinding key will provide the PI with the unblinding codes for individuals that have completed 
enrollment. Data analysis will be performed by trainees for educational purposes (e.g. graduate students, 
postdoctoral fellows) as well as the laboratory manager for preparation of DSMB reports and assessment 
of any disparities in the demographic distribution between the active and placebo groups. Final analysis 
will occur when all participants have finished the final follow-up phase of the study.  
 
Responsibility for Data and Safety Monitoring: The PI, protocol-approved research team, and 
ME/DSMB are all responsible for data and safety monitoring. The PI will be most involved in data and 
safety oversight. The PI will discuss data integrity and inquire about safety/participant tolerance in 
weekly meetings with the research team.  
 
Data Entry Methods: Data will be collected using REDCap™, which is a secure web application for 
building and managing online surveys and databases. REDcap™ supports online or offline data capture 
for research studies and operations. Participants and protocol-approved study personnel will enter data 
directly into the online portal to ensure security and prevent data loss.  
 
Data Analysis Plan: Data for this study (behavioral assessments, functional MRI measurements) will be 
acquired by protocol-approved members of the research team, including graduate students and research 
specialists. These individuals will also perform data management and analysis under the guidance of the 
PI. Deidentified data will be shared per NIH requirements. Manuscript composition will be led by the PI 
and Co-Is, with the assistance of the research team.   
 
Quality Assurance Plan: Weekly meetings will be held between the PIs and research team to discuss 
any data-related problems as well as qualitative comments received during data collection. Initial data 
analyses will examine distributions of variable scores, and comparability of baseline characteristics across 
conditions, any necessary adjustments to analyses will be made. Confidentiality protections are outlined 
below.  
 
Review of the study will be conducted annually by the PI and laboratory manager  (including enrollment, 
retention, assessment inventories) and discussed with the DSMB. Data collected in previous studies by 
our research group have demonstrated that after extended use in the MRI scanner environment (likely 
more than 5000 pulses) the strength of the induced magnetic field from the biphasic coil begins to drop in 
a non-linear fashion. Consequently, the intensity of the induced magnetic field from the coil will be 
assessed by protocol-approved study personnel and logged weekly (alongside with protocol use, number 
of pulses, intensity of pulses). This cumulative record of coil performance will be monitored and, when 
the intensity of the induced field had degraded 10%, we will switch to a new, identical coil.   
 

Protocol version: 4.4 
Protocol date: 06/10/2022 
22 
Definition and Reporting of AEs/SAEs to the IRB/NIDA: An adverse event (AE) is defined as any 
untoward medical occurrence in a study subject who was administered rTMS but does not necessarily 
have a causal relationship with this treatment. Any unwanted change, physically, psychologically or 
behaviorally, that occurs in a study participant during the course of the trial is an adverse event. A Serious 
Adverse Event (SAE) is defined as an adverse event that has one of the following outcomes: death, life-
threatening, inparticipant hospitalization or prolongation of existing hospitalization, persistent or 
significant disability/incapacity, a congenital anomaly/birth defect.  
 
All unexpected AEs will be reported to the Wake Forest Institutional Review Board (IRB) and Committee 
on Human Research within 48-business hours. Serious AEs will also be reported within 24-business 
hours. Follow-up of all unexpected and serious AEs will also be reported to these agencies. AEs/SAEs are 
documented and reported as per IRB requirements. Research staff will identify AEs and obtain all 
available information to assess severity, seriousness, study relatedness, expectedness, outcome, and the 
need for change or discontinuation in the study intervention. AEs are documented on AE Logs and AE 
Case Report Forms. Additional relevant AE information, if available, will be documented in a progress 
note and stored in the research record as appropriate to allow monitoring and further evaluation. If the AE 
meets the definition for serious, appropriate SAE protocol specific reporting forms are completed and 
disseminated to the appropriate persons and within the designated timeframes as indicated above. For 
each AE/SAE recorded, the research staff will follow the AE/SAE until resolution, stabilization, or until 
the participant is no longer in the study as stated in the protocol. We will report adverse events to the 
Medical Wake Forest IRB online per the IRB’s guidelines.   
 
Collection and Reporting of AEs and SAEs: As mentioned above, all AEs/SAEs are documented and 
reported as per IRB requirements. Research staff will identify AEs, verify event with the participant, and 
obtain all available information to assess severity, seriousness, study relatedness, expectedness, outcome, 
and the need for change or discontinuation in the study intervention. AEs are documented on AE Logs 
and AE Case Report Forms. Additional relevant AE information, if available, will be documented in a 
progress note and stored in the research record as appropriate to allow monitoring and further evaluation. 
If the AE meets the definition for serious, appropriate SAE protocol specific reporting forms are 
completed and disseminated to the appropriate persons and within the designated timeframes as indicated 
above. If applicable, copies of medical records and injury reports will be retrieved and safely stored in the 
subjects file. De-identified copies of reports will be sent to the Wake Forest IRB, ME/DSBM, and NIH. 
For each AE/SAE recorded, the research staff will follow the AE/SAE until resolution, stabilization, or 
until the participant is no longer in the study as stated in the protocol.  
 
Reporting of Unanticipated Problems, Adverse Events or Deviations: Any unanticipated problems, 
serious, and/or unexpected AEs, deviations or protocol changes will be reported within 24-72 business 
hours, depending on severity, by the principal investigator or designated member of the research team to 
the Wake Forest IRB, ME/DSMB and to the sponsor, the NIH.  
 
Management of SAEs or Other Study Risks: As described above, SAEs will be immediately reported, 
within 24 business hours, to the ME/DSBM, sponsor and Wake Forest IRB. For each SAE recorded, the 
research staff will follow the SAE until resolution, stabilization, or until the participant is no longer in the 
study as stated in the protocol. If applicable, copies of medical records and injury reports will be retrieved 
and safely stored in the subjects file. De-identified copies of reports will be sent to the Wake Forest IRB,  
ME/DSBM, and NIH. 
 
Reporting of IRB Actions and ME/DSMB Reports to NIAAA: Any IRB actions and ME/DSMB 
reports will be reported to both the Wake Forest IRB and the NIH Institute supporting the study following 
the sponsor’s report submission guidelines, should this study be awarded.  
 

Protocol version: 4.4 
Protocol date: 06/10/2022 
23 
Report of Changes or Amendments to the Protocol: Any changes to the proposal/protocol must be 
approved by the NIH Institute supporting the study. Any amendments to the IRB protocol associated with 
the proposed work will be reported to NIH should this proposal be awarded funding.  
 
Trial Stopping Rules: The protocol will immediately be paused following notification of a SAE. Per 
IRB policy, the IRB and ME/DSMB will be notified within 24 business hours following the SAE 
notification. SAEs will be reported to NIH within 72 hours. Should the reported SAE be confirmed as 
directly related to the protocol, the trial will be terminated. The device manufacturer will be notified 
within 72 business hours. Of note, according to the literature associated with the MagVenture device, 
there have been no clinical trials stopped or SAEs reported.   
 
Conflict of Interest: Neither the PI, nor members of the research team have any Conflicts of Interest 
directly related to this protocol. The rTMS device used for the proposed study is manufactured by 
MagVenture. 
 
References 
1. 
Haber SN, Knutson B. The reward circuit: linking primate anatomy and human imaging. 
Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology. 
2010;35(1):4-26. doi: 10.1038/npp.2009.129. PubMed PMID: 19812543; PubMed Central PMCID: 
PMC3055449. 
2. 
Bickel WK, Snider SE, Quisenberry AJ, Stein JS, Hanlon CA. Competing neurobehavioral 
decision systems theory of cocaine addiction: From mechanisms to therapeutic opportunities. Progress in 
brain research. 2016;223:269-93. doi: 10.1016/bs.pbr.2015.07.009. PubMed PMID: 26806781. 
3. 
Huang YZ, Edwards MJ, Rounis E, Bhatia KP, Rothwell JC. Theta burst stimulation of the 
human motor cortex. Neuron. 2005;45(2):201-6. doi: 10.1016/j.neuron.2004.12.033. PubMed PMID: 
15664172. 
4. 
Amiaz R, Levy D, Vainiger D, Grunhaus L, Zangen A. Repeated high-frequency transcranial 
magnetic stimulation over the dorsolateral prefrontal cortex reduces cigarette craving and consumption. 
Addiction. 2009;104(4):653-60. doi: 10.1111/j.1360-0443.2008.02448.x. PubMed PMID: 19183128. 
5. 
Dinur-Klein L, Dannon P, Hadar A, Rosenberg O, Roth Y, Kotler M, Zangen A. Smoking 
cessation induced by deep repetitive transcranial magnetic stimulation of the prefrontal and insular 
cortices: a prospective, randomized controlled trial. Biological psychiatry. 2014;76(9):742-9. doi: 
10.1016/j.biopsych.2014.05.020. PubMed PMID: 25038985. 
6. 
Eichhammer P, Johann M, Kharraz A, Binder H, Pittrow D, Wodarz N, Hajak G. High-frequency 
repetitive transcranial magnetic stimulation decreases cigarette smoking. The Journal of clinical 
psychiatry. 2003;64(8):951-3. PubMed PMID: 12927012. 
7. 
Li X, Hartwell KJ, Owens M, Lematty T, Borckardt JJ, Hanlon CA, Brady KT, George MS. 
Repetitive transcranial magnetic stimulation of the dorsolateral prefrontal cortex reduces nicotine cue 
craving. Biological psychiatry. 2013;73(8):714-20. doi: 10.1016/j.biopsych.2013.01.003. PubMed PMID: 
23485014; PubMed Central PMCID: PMC3615051. 
8. 
Pripfl J, Tomova L, Riecansky I, Lamm C. Transcranial magnetic stimulation of the left 
dorsolateral prefrontal cortex decreases cue-induced nicotine craving and EEG delta power. Brain 
stimulation. 2014;7(2):226-33. doi: 10.1016/j.brs.2013.11.003. PubMed PMID: 24468092. 
9. 
Wing VC, Bacher I, Wu BS, Daskalakis ZJ, George TP. High frequency repetitive transcranial 
magnetic stimulation reduces tobacco craving in schizophrenia. Schizophrenia research. 2012;139(1-
3):264-6. doi: 10.1016/j.schres.2012.03.006. PubMed PMID: 22464727. 
10. 
Hanlon CA, Canterberry M, Taylor JJ, DeVries W, Li X, Brown TR, George MS. Probing the 
frontostriatal loops involved in executive and limbic processing via interleaved TMS and functional MRI 
at two prefrontal locations: a pilot study. PloS one. 2013;8(7):e67917. doi: 
10.1371/journal.pone.0067917. PubMed PMID: 23874466; PubMed Central PMCID: PMC3706588. 

Protocol version: 4.4 
Protocol date: 06/10/2022 
24 
11. 
Hanlon CA, Hartwell KJ, Canterberry M, Li X, Owens M, Lematty T, Prisciandaro JJ, Borckardt 
J, Brady KT, George MS. Reduction of cue-induced craving through realtime neurofeedback in nicotine 
users: the role of region of interest selection and multiple visits. Psychiatry research. 2013;213(1):79-81. 
doi: 10.1016/j.pscychresns.2013.03.003. PubMed PMID: 23683344; PubMed Central PMCID: 
PMCPMC4093788. 
12. 
Hartwell KJ, Hanlon CA, Li X, Borckardt JJ, Canterberry M, Prisciandaro JJ, Moran-Santa Maria 
MM, LeMatty T, George MS, Brady KT. Individualized real-time fMRI neurofeedback to attenuate 
craving in nicotine-dependent smokers. Journal of psychiatry & neuroscience : JPN. 2016;41(1):48-55. 
PubMed PMID: 26505139; PubMed Central PMCID: PMC4688028. 
13. 
Moran-Santa Maria MM, Hartwell KJ, Hanlon CA, Canterberry M, Lematty T, Owens M, Brady 
KT, George MS. Right anterior insula connectivity is important for cue-induced craving in nicotine-
dependent smokers. Addict Biol. 2015;20(2):407-14. doi: 10.1111/adb.12124. PubMed PMID: 24529072; 
PubMed Central PMCID: PMC4133311. 
14. 
United States. Public Health Service. Office of the Surgeon General. The health consequences of 
smoking--50 years of progress : a report of the surgeon general. Rockville, MD: U.S. Department of 
Health and Human Services, Public Health Service, Office of the Surgeon General; 2014. 2 volumes p. 
15. 
Xu X, Bishop EE, Kennedy SM, Simpson SA, Pechacek TF. Annual healthcare spending 
attributable to cigarette smoking: an update. Am J Prev Med. 2015;48(3):326-33. doi: 
10.1016/j.amepre.2014.10.012. PubMed PMID: 25498551; PubMed Central PMCID: PMCPMC4603661. 
16. 
Cahill K, Stevens S, Perera R, Lancaster T. Pharmacological interventions for smoking cessation: 
an overview and network meta-analysis. Cochrane Database Syst Rev. 2013(5):CD009329. doi: 
10.1002/14651858.CD009329.pub2. PubMed PMID: 23728690. 
17. 
Cao ZF, Burdakov D, Sarnyai Z. Optogenetics: potentials for addiction research. Addict Biol. 
2011;16(4):519-31. doi: 10.1111/j.1369-1600.2011.00386.x. PubMed PMID: 21929708. 
18. 
Steinberg EE, Janak PH. Establishing causality for dopamine in neural function and behavior 
with optogenetics. Brain research. 2013;1511:46-64. doi: 10.1016/j.brainres.2012.09.036. PubMed PMID: 
23031636; PubMed Central PMCID: PMC3654072. 
19. 
Ferguson SM, Neumaier JF. Grateful DREADDs: engineered receptors reveal how neural circuits 
regulate behavior. Neuropsychopharmacology : official publication of the American College of 
Neuropsychopharmacology. 2012;37(1):296-7. doi: 10.1038/npp.2011.179. PubMed PMID: 22157861; 
PubMed Central PMCID: PMC3238068. 
20. 
Peters J, LaLumiere RT, Kalivas PW. Infralimbic prefrontal cortex is responsible for inhibiting 
cocaine seeking in extinguished rats. The Journal of neuroscience : the official journal of the Society for 
Neuroscience. 2008;28(23):6046-53. doi: 10.1523/JNEUROSCI.1045-08.2008. PubMed PMID: 
18524910; PubMed Central PMCID: PMC2585361. 
21. 
Barbas H. Pattern in the cortical distribution of prefrontally directed neurons with divergent axons 
in the rhesus monkey. Cereb Cortex. 1995;5(2):158-65. PubMed PMID: 7620292. 
22. 
Groenewegen HJ, Uylings HB. The prefrontal cortex and the integration of sensory, limbic and 
autonomic information. Progress in brain research. 2000;126:3-28. doi: 10.1016/S0079-6123(00)26003-2. 
PubMed PMID: 11105636. 
23. 
Cho SS, Strafella AP. rTMS of the left dorsolateral prefrontal cortex modulates dopamine release 
in the ipsilateral anterior cingulate cortex and orbitofrontal cortex. PloS one. 2009;4(8):e6725. doi: 
10.1371/journal.pone.0006725. PubMed PMID: 19696930; PubMed Central PMCID: PMC2725302. 
24. 
Strafella AP. Striatal dopamine release induced by repetitive transcranial magnetic stimulation of 
the human motor cortex. Brain. 2003;126(12):2609--15. doi: 10.1093/brain/awg268. PubMed PMID: 
Strafella2003. 
25. 
Siebner HR, Bergmann TO, Bestmann S, Massimini M, Johansen-Berg H, Mochizuki H, 
Bohning DE, Boorman ED, Groppa S, Miniussi C, Pascual-Leone A, Huber R, Taylor PC, Ilmoniemi RJ, 
De Gennaro L, Strafella AP, Kahkonen S, Kloppel S, Frisoni GB, George MS, Hallett M, Brandt SA, 
Rushworth MF, Ziemann U, Rothwell JC, Ward N, Cohen LG, Baudewig J, Paus T, Ugawa Y, Rossini 

Protocol version: 4.4 
Protocol date: 06/10/2022 
25 
PM. Consensus paper: combining transcranial stimulation with neuroimaging. Brain stimulation. 
2009;2(2):58-80. Epub 2009/04/01. doi: S1935-861X(08)00361-6 [pii] 
10.1016/j.brs.2008.11.002. PubMed PMID: 20633405. 
26. 
Mueller JK, Grigsby EM, Prevosto V, Petraglia FW, 3rd, Rao H, Deng ZD, Peterchev AV, 
Sommer MA, Egner T, Platt ML, Grill WM. Simultaneous transcranial magnetic stimulation and single-
neuron recording in alert non-human primates. Nat Neurosci. 2014;17(8):1130-6. doi: 10.1038/nn.3751. 
PubMed PMID: 24974797; PubMed Central PMCID: PMC4115015. 
27. 
Hanlon CA, Dowdle LT, Moss H, Canterberry M, George MS. Mobilization of Medial and 
Lateral Frontal-Striatal Circuits in Cocaine Users and Controls: An Interleaved TMS/BOLD Functional 
Connectivity Study. Neuropsychopharmacology : official publication of the American College of 
Neuropsychopharmacology. 2016. doi: 10.1038/npp.2016.114. PubMed PMID: 27374278. 
28. 
Bickel WK, Yi R, Kowal BP, Gatchalian KM. Cigarette smokers discount past and future rewards 
symmetrically and more than controls: is discounting a measure of impulsivity? Drug and alcohol 
dependence. 2008;96(3):256-62. doi: 10.1016/j.drugalcdep.2008.03.009. PubMed PMID: 18468814; 
PubMed Central PMCID: PMCPMC2701143. 
29. 
Bickel WK, Miller ML, Yi R, Kowal BP, Lindquist DM, Pitcock JA. Behavioral and 
neuroeconomics of drug addiction: competing neural systems and temporal discounting processes. Drug 
and alcohol dependence. 2007;90 Suppl 1:S85-91. Epub 2006/11/15. doi: 
10.1016/j.drugalcdep.2006.09.016. PubMed PMID: 17101239; PubMed Central PMCID: PMC2033431. 
30. 
Bechara A. Decision making, impulse control and loss of willpower to resist drugs: a 
neurocognitive perspective. Nat Neurosci. 2005;8(11):1458-63. doi: 10.1038/nn1584. PubMed PMID: 
16251988. 
31. 
Mitchell SH. Measures of impulsivity in cigarette smokers and non-smokers. 
Psychopharmacology. 1999;146(4):455--64. doi: 10.1007/PL00005491. PubMed PMID: Mitchell1999. 
32. 
Baker F, Johnson MW, Bickel WK. Delay discounting in current and never-before cigarette 
smokers: Similarities and differences across commodity, sign, and magnitude. J Abnorm Psychol. 
2003;112(3):382--92. doi: 10.1037/0021-843X.112.3.382. PubMed PMID: Baker2003. 
33. 
Bickel WK, Odum AL, Madden GJ. Impulsivity and cigarette smoking: delay discounting in 
current, never, and ex-smokers. Psychopharmacology. 1999;146(4):447--54. doi: 10.1007/PL00005490. 
PubMed PMID: Bickel1999. 
34. 
Reynolds B, Richards JB, Horn K, Karraker K. Delay discounting and probability discounting as 
related to cigarette smoking status in adults. Behavioural Processes. 2004;65(1):35--42. doi: 
10.1016/S0376-6357(03)00109-8. PubMed PMID: Reynolds2004. 
35. 
Ohmura Y, Takahashi T, Kitamura N. Discounting Delayed and Probabilistic Monetary Gains 
and Losses by Smokers of Cigarettes2016:179--96. doi: 10.1007/978-4-431-55402-8_8. 
36. 
Amlung M, Petker T, Jackson J, Balodis I, MacKillop J. Steep discounting of delayed monetary 
and food rewards in obesity: a meta-analysis. Psychological medicine. 2016;46(11):2423--34. doi: 
10.1017/S0033291716000866. PubMed PMID: Amlung2016. 
37. 
MacKillop J, Amlung MT, Few LR, Ray LA, Sweet LH, Munaf. Delayed reward discounting and 
addictive behavior: a meta-analysis. Psychopharmacology. 2011;216(3):305--21. doi: 10.1007/s00213-
011-2229-0. PubMed PMID: MacKillop2011. 
38. 
Bickel WK, Koffarnus MN, Moody L, Wilson AG. The behavioral- and neuro-economic process 
of temporal discounting: A candidate behavioral marker of addiction. Neuropharmacology. 2014;76 Pt 
B:518-27. doi: 10.1016/j.neuropharm.2013.06.013. PubMed PMID: 23806805; PubMed Central PMCID: 
PMCPMC3858579. 
39. 
Sheffer CE, Christensen DR, Landes R, Carter LP, Jackson L, Bickel WK. Delay discounting 
rates: a strong prognostic indicator of smoking relapse. Addict Behav. 2014;39(11):1682-9. doi: 
10.1016/j.addbeh.2014.04.019. PubMed PMID: 24878037; PubMed Central PMCID: PMCPMC4316833. 
40. 
Sheffer C, Mackillop J, McGeary J, Landes R, Carter L, Yi R, Jones B, Christensen D, Stitzer M, 
Jackson L, Bickel W. Delay discounting, locus of control, and cognitive impulsiveness independently 
predict tobacco dependence treatment outcomes in a highly dependent, lower socioeconomic group of 

Protocol version: 4.4 
Protocol date: 06/10/2022 
26 
smokers. The American journal on addictions / American Academy of Psychiatrists in Alcoholism and 
Addictions. 2012;21(3):221-32. doi: 10.1111/j.1521-0391.2012.00224.x. PubMed PMID: 22494224; 
PubMed Central PMCID: PMCPMC3567840. 
41. 
Stein JS, Wilson AG, Koffarnus MN, Daniel TO, Epstein LH, Bickel WK. Unstuck in time: 
episodic future thinking reduces delay discounting and cigarette smoking. Psychopharmacology. 2016:1--
8. doi: 10.1007/s00213-016-4410-y. PubMed PMID: Stein2016. 
42. 
Hursh SR. Behavioral economics. Journal of the Experimental Analysis of Behavior. 
1984;42(3):435-52. doi: 10.1901/jeab.1984.42-435. 
43. 
Hursh SR, Silberberg A. Economic demand and essential value. Psychological Review. 
2008;115(1):186--98. doi: 10.1037/0033-295X.115.1.186. PubMed PMID: Hursh2008. 
44. 
Murphy JG, MacKillop J, Tidey JW, Brazil LA, Colby SM. Validity of a demand curve measure 
of nicotine reinforcement with adolescent smokers. Drug and alcohol dependence. 2011;113(2-3):207--
14. doi: 10.1016/j.drugalcdep.2010.08.004. PubMed PMID: Murphy2011. 
45. 
Cox LS, Tiffany ST, Christen AG. Evaluation of the brief questionnaire of smoking urges (QSU-
brief) in laboratory and clinical settings. Nicotine Tob Res. 2001;3(1):7-16. Epub 2001/03/22. doi: 
10.1080/14622200020032051. PubMed PMID: 11260806. 
46. 
Bakker N, Shahab S, Giacobbe P, Blumberger DM, Daskalakis ZJ, Kennedy SH, Downar J. 
rTMS of the dorsomedial prefrontal cortex for major depression: safety, tolerability, effectiveness, and 
outcome predictors for 10 Hz versus intermittent theta-burst stimulation. Brain stimulation. 
2015;8(2):208-15. doi: 10.1016/j.brs.2014.11.002. PubMed PMID: 25465290. 
47. 
Bellamoli E, Manganotti P, Schwartz RP, Rimondo C, Gomma M, Serpelloni G. rTMS in the 
treatment of drug addiction: an update about human studies. Behavioural neurology. 2014;2014:815215. 
doi: 10.1155/2014/815215. PubMed PMID: 24803733; PubMed Central PMCID: PMC4006612. 
48. 
Gorelick DA, Zangen A, George MS. Transcranial magnetic stimulation in the treatment of 
substance addiction. Ann N Y Acad Sci. 2014;1327:79-93. doi: 10.1111/nyas.12479. PubMed PMID: 
25069523; PubMed Central PMCID: PMCPMC4206564. 
49. 
Barr MS, Farzan F, Wing VC, George TP, Fitzgerald PB, Daskalakis ZJ. Repetitive transcranial 
magnetic stimulation and drug addiction. International review of psychiatry. 2011;23(5):454-66. doi: 
10.3109/09540261.2011.618827. PubMed PMID: 22200135. 
50. 
Wing VC, Barr MS, Wass CE, Lipsman N, Lozano AM, Daskalakis ZJ, George TP. Brain 
stimulation methods to treat tobacco addiction. Brain stimulation. 2013;6(3):221-30. doi: 
10.1016/j.brs.2012.06.008. PubMed PMID: 22809824. 
51. 
Feil J, Zangen A. Brain stimulation in the study and treatment of addiction. Neuroscience and 
biobehavioral reviews. 2010;34(4):559-74. doi: 10.1016/j.neubiorev.2009.11.006. PubMed PMID: 
19914283. 
52. 
Denslow S, Lomarev M, George MS, Bohning DE. Cortical and subcortical brain effects of 
transcranial magnetic stimulation (TMS)-induced movement: an interleaved TMS/functional magnetic 
resonance imaging study. Biological psychiatry. 2005;57(7):752-60. Epub 2005/04/12. doi: 
10.1016/j.biopsych.2004.12.017. PubMed PMID: 15820232. 
53. 
Fox MD, Buckner RL, White MP, Greicius MD, Pascual-Leone A. Efficacy of transcranial 
magnetic stimulation targets for depression is related to intrinsic functional connectivity with the 
subgenual cingulate. Biological psychiatry. 2012;72(7):595-603. Epub 2012/06/05. doi: S0006-
3223(12)00411-8 [pii] 
10.1016/j.biopsych.2012.04.028. PubMed PMID: 22658708. 
54. 
Bohning DE, Shastri A, Lomarev MP, Lorberbaum JP, Nahas Z, George MS. BOLD-fMRI 
response vs. transcranial magnetic stimulation (TMS) pulse-train length: testing for linearity. Journal of 
magnetic resonance imaging : JMRI. 2003;17(3):279-90. Epub 2003/02/21. doi: 10.1002/jmri.10271. 
PubMed PMID: 12594717. 
55. 
Bestmann S, Baudewig J, Siebner HR, Rothwell JC, Frahm J. Functional MRI of the immediate 
impact of transcranial magnetic stimulation on cortical and subcortical motor circuits. The European 

Protocol version: 4.4 
Protocol date: 06/10/2022 
27 
journal of neuroscience. 2004;19(7):1950-62. Epub 2004/04/14. doi: 10.1111/j.1460-9568.2004.03277.x. 
PubMed PMID: 15078569. 
56. 
Strafella AP, Paus T, Barrett J, Dagher A. Repetitive transcranial magnetic stimulation of the 
human prefrontal cortex induces dopamine release in the caudate nucleus. The Journal of neuroscience : 
the official journal of the Society for Neuroscience. 2001;21(15):RC157. PubMed PMID: 11459878. 
57. 
Di Lazzaro V, Pilato F, Saturno E, Oliviero A, Dileone M, Mazzone P, Insola A, Tonali PA, 
Ranieri F, Huang YZ, Rothwell JC. Theta-burst repetitive transcranial magnetic stimulation suppresses 
specific excitatory circuits in the human motor cortex. The Journal of physiology. 2005;565(Pt 3):945-50. 
doi: 10.1113/jphysiol.2005.087288. PubMed PMID: 15845575; PubMed Central PMCID: PMC1464561. 
58. 
Bear MF, Malenka RC. Synaptic plasticity: LTP and LTD. Current opinion in neurobiology. 
1994;4(3):389-99. PubMed PMID: 7919934. 
59. 
Malenka RC, Bear MF. LTP and LTD: an embarrassment of riches. Neuron. 2004;44(1):5-21. 
doi: 10.1016/j.neuron.2004.09.012. PubMed PMID: 15450156. 
60. 
Corrigall WA, Coen KM, Adamson KL. Self-administered nicotine activates the mesolimbic 
dopamine system through the ventral tegmental area. Brain Research. 1994;653(1-2):278-84. doi: 
10.1016/0006-8993(94)90401-4. 
61. 
Barrett SP, Boileau I, Okker J, Pihl RO, Dagher A. The hedonic response to cigarette smoking is 
proportional to dopamine release in the human striatum as measured by positron emission tomography 
and [11C]raclopride. Synapse. 2004;54(2):65-71. doi: 10.1002/syn.20066. PubMed PMID: 15352131. 
62. 
Marenco S, Carson RE, Berman KF, Herscovitch P, Weinberger DR. Nicotine-induced dopamine 
release in primates measured with [11C]raclopride PET. Neuropsychopharmacology : official publication 
of the American College of Neuropsychopharmacology. 2004;29(2):259-68. doi: 
10.1038/sj.npp.1300287. PubMed PMID: 14666115. 
63. 
Hanlon CA, Dowdle LT, Austelle CW, DeVries W, Mithoefer O, Badran BW, George MS. What 
goes up, can come down: Novel brain stimulation paradigms may attenuate craving and craving-related 
neural circuitry in substance dependent individuals. Brain research. 2015. doi: 
10.1016/j.brainres.2015.02.053. PubMed PMID: 25770818. 
64. 
Bickel WK, Jarmolowicz DP, Mueller ET, Gatchalian KM. The behavioral economics and 
neuroeconomics of reinforcer pathologies: implications for etiology and treatment of addiction. Curr 
Psychiatry Rep. 2011;13(5):406-15. doi: 10.1007/s11920-011-0215-1. 
65. 
Bickel WK, Marsch LA, Buchhalter AR, Badger GJ. Computerized behavior therapy for opioid-
dependent out: a randomized controlled trial. Exp Clin Psychopharmacol. 2008;16(2):132-43. doi: 
10.1037/1064-1297.16.2.132. 
66. 
Stein JS, Madden GJ. Delay Discounting and Drug Abuse: Empirical, Conceptual, and 
Methodological Considerations.  The Wiley-Blackwell Handbook of Addiction Psychopharmacology: 
Wiley-Blackwell; 2013. p. 165-208. 
67. 
Yi R, Buchhalter AR, Gatchalian KM, Bickel WK. The relationship between temporal 
discounting and the prisoner's dilemma game in intranasal abusers of prescription opioids. Drug Alcohol 
Depend. 2007;87(1):94-7. doi: 10.1016/j.drugalcdep.2006.07.007. 
68. 
Hanlon CA, Beveridge TJ, Porrino LJ. Recovering from cocaine: Insights from clinical and 
preclinical investigations. Neuroscience and biobehavioral reviews. 2013. Epub 2013/05/01. doi: 
10.1016/j.neubiorev.2013.04.007. PubMed PMID: 23628740. 
69. 
Hanlon CA, Canterberry M. The use of brain imaging to elucidate neural circuit changes in 
cocaine addiction. Subst Abuse Rehabil. 2012;3(1):115-28. Epub 2012/11/20. doi: 10.2147/SAR.S35153. 
PubMed PMID: 23162375. 
70. 
Hanlon CA, DeVries W, Dowdle LT, West JA, Siekman B, Li X, George MS. A comprehensive 
study of sensorimotor cortex excitability in chronic cocaine users: Integrating TMS and functional MRI 
data. Drug and alcohol dependence. 2015. doi: 10.1016/j.drugalcdep.2015.07.1196. PubMed PMID: 
26541870. 

Protocol version: 4.4 
Protocol date: 06/10/2022 
28 
71. 
Hanlon CA, Dufault DL, Wesley MJ, Porrino LJ. Elevated gray and white matter densities in 
cocaine abstainers compared to current users. Psychopharmacology. 2011;218(4):681-92. Epub 
2011/06/22. doi: 10.1007/s00213-011-2360-y. PubMed PMID: 21691942. 
72. 
Hanlon CA, Wesley MJ, Porrino LJ. Loss of functional specificity in the dorsal striatum of 
chronic cocaine users. Drug and alcohol dependence. 2009;102(1-3):88-94. doi: 
10.1016/j.drugalcdep.2009.01.005. PubMed PMID: 19264428; PubMed Central PMCID: PMC3124239. 
73. 
Hanlon CA, Wesley MJ, Roth AJ, Miller MD, Porrino LJ. Loss of laterality in cocaine users: an 
fMRI investigation of sensorimotor control. Neuropsychopharmacology : official publication of the 
American College of Neuropsychopharmacology. 2009;in press. 
74. 
Dunlop K, Hanlon CA, Downar J. Noninvasive brain stimulation treatments for addiction and 
major depression. Annals of the New York Academy of Sciences. 2016. doi: 10.1111/nyas.12985. 
PubMed PMID: 26849183. 
75. 
Higgins S, Budney A, Bickel W. Applying behavioral concepts and principles to the treatment of 
cocaine dependence. Drug and alcohol dependence. 1994;34(2):87-97. doi: 10.1016/0376-
8716(94)90128-7. 
76. 
Marsch LA, Bickel WK, Badger GJ, Jacobs EA. Buprenorphine treatment for opioid dependence: 
the relative efficacy of daily, twice and thrice weekly dosing. Drug and alcohol dependence. 
2005;77(2):195-204. doi: 10.1016/j.drugalcdep.2004.08.011. PubMed PMID: 15664721. 
77. 
Stanger C, Ryan SR, Fu H, Landes RD, Jones BA, Bickel WK, Budney AJ. Delay discounting 
predicts adolescent substance abuse treatment outcome. Exp Clin Psychopharmacol. 2012;20(3):205-12. 
doi: 10.1037/a0026543. PubMed PMID: 22182419; PubMed Central PMCID: PMCPMC3906638. 
78. 
Hanlon CA, Dowdle LT, Naselaris T, Canterberry M, Cortese BM. Visual cortex activation to 
drug cues: A meta-analysis of functional neuroimaging papers in addiction and substance abuse literature. 
Drug and alcohol dependence. 2014;143:206-12. doi: 10.1016/j.drugalcdep.2014.07.028. PubMed PMID: 
25155889; PubMed Central PMCID: PMC4161649. 
79. 
Giordano LA, Bickel WK, Shahan TA, Badger GJ. Behavioral economics of human drug self‐
administration: progressive ratio versus random sequences of response requirements. Behav Pharmacol. 
2001;12(5):343. 
80. 
Bickel WK, Marsch LA. Toward a behavioral economic understanding of drug dependence: delay 
discounting processes. Addiction. 2001;96(1):73-86. doi: 10.1080/09652140020016978. 
81. 
Bickel WK, Marsch LA, Carroll ME. Deconstructing relative reinforcing efficacy and situating 
the measures of pharmacological reinforcement with behavioral economics: a theoretical proposal. 
Psychopharmacology. 2000;153(1):44-56. 
82. 
Stein JS, Daniel TO, Epstein LH, Bickel WK. Episodic future thinking reduces delay discounting 
in cigarette smokers. Drug and alcohol dependence. 2015;156:e212. doi: 
10.1016/j.drugalcdep.2015.07.571. 
83. 
Stein JS, Johnson PS, Renda CR, Smits RR, Liston KJ, Shahan TA, Madden GJ. Early and 
prolonged exposure to reward delay: effects on impulsive choice and alcohol self-administration in male 
rats. Exp Clin Psychopharmacol. 2013;21(2):172-80. doi: 10.1037/a0031245. 
84. 
Stein JS, Koffarnus MN, Snider SE, Quisenberry AJ, Bickel WK. Identification and management 
of nonsystematic purchase task data: Toward best practice. Exp Clin Psychopharmacol. 2015;23(5):377. 
85. 
Snider SE, LaConte SM, Bickel WK. Episodic Future Thinking: Expansion of the Temporal 
Window in Individuals with Alcohol Dependence. Alcohol Clin Exp Res. 2016;40(7):1558-66. doi: 
10.1111/acer.13112. 
86. 
Wilson AG, Franck CT, Koffarnus MN, Bickel WK. Behavioral Economics of Cigarette Purchase 
Tasks: Within-Subject Comparison of Real, Potentially Real, and Hypothetical Cigarettes. Nicotine Tob 
Res. 2016;18(5):524-30. doi: 10.1093/ntr/ntv154. 
87. 
Bickel WK, Mellis AM, Snider SE, Moody L, Stein JS, Quisenberry AJ. Novel Therapeutics for 
Addiction: Behavioral Economic and Neuroeconomic Approaches. Current Treatment Options in 
Psychiatry. 2016;3(3):277-92. 

Protocol version: 4.4 
Protocol date: 06/10/2022 
29 
88. 
Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, Hergueta T, Baker R, 
Dunbar GC. The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and 
validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. The Journal of 
clinical psychiatry. 1998;59 Suppl 20:22-33;quiz 4-57. PubMed PMID: 9881538. 
89. 
Sobell LC, Sobell MB. Timeline Follow-Back. In: Litten RZ, Allen JP, editors. Measuring 
Alcohol Consumption: Psychosocial and Biochemical Methods. Totowa, NJ: Humana Press; 1992. p. 41-
72. 
90. 
Beck AT, Steer RA, Brown GK. Manual for the Beck Depression Inventory-II. San Antonio, TX: 
Psychological Corporation; 1996. 
91. 
Spielberger CD, Gorsuch RL, Lushene RH. State-trait Anxiety Inventory. Palo Alto: Consulting 
Psychologists Press; 1970. 
92. 
M. MD, M. L, F. DL. Profile of Mood States (POMS)–Revised Manual. San Diego, CA: 
Education and Industrial Testing Service; 1992. 
93. 
McClernon FJ, Hiott FB, Huettel SA, Rose JE. Abstinence-induced changes in self-report craving 
correlate with event-related FMRI responses to smoking cues. Neuropsychopharmacology : official 
publication of the American College of Neuropsychopharmacology. 2005;30(10):1940-7. Epub 
2005/05/28. doi: 1300780 [pii] 
10.1038/sj.npp.1300780. PubMed PMID: 15920499. 
94. 
McClernon FJ, Kozink RV, Rose JE. Individual differences in nicotine dependence, withdrawal 
symptoms, and sex predict transient fMRI-BOLD responses to smoking cues. Neuropsychopharmacology 
: official publication of the American College of Neuropsychopharmacology. 2008;33(9):2148-57. 
PubMed PMID: 17987060. 
95. 
Henningfield JE, Stitzer ML, Griffiths RR. Expired air carbon monoxide accumulation and 
elimination as a function of number of cigarettes smoked. Addictive Behaviors. 1980;5(3):265-72. doi: 
10.1016/0306-4603(80)90049-0. 
96. 
Fagerstrom KO, Schneider NG. Measuring nicotine dependence: A review of the Fagerstrom 
Tolerance Questionnaire. Journal of Behavioral Medicine. 1989;12(2):159-82. doi: 10.1007/bf00846549. 
97. 
Fagerstrom K. Determinants of tobacco use and renaming the FTND to the Fagerstrom Test for 
Cigarette Dependence. Nicotine Tob Res. 2012;14(1):75-8. doi: 10.1093/ntr/ntr137. PubMed PMID: 
22025545. 
98. 
Jacobs EA, Bickel WK. Modeling drug consumption in the clinic using simulation procedures: 
Demand for heroin and cigarettes in opioid-dependent outpatients. Experimental and clinical 
psychopharmacology. 1999;7(4):412-26. doi: 10.1037/1064-1297.7.4.412. 
99. 
MacKillop J, Murphy JG, Ray LA, Eisenberg DT, Lisman SA, Lum JK, Wilson DS. Further 
validation of a cigarette purchase task for assessing the relative reinforcing efficacy of nicotine in college 
smokers. Exp Clin Psychopharmacol. 2008;16(1):57-65. doi: 10.1037/1064-1297.16.1.57. PubMed 
PMID: 18266552. 
100. 
MacKillop J, Murphy JG. A behavioral economic measure of demand for alcohol predicts brief 
intervention outcomes. Drug Alcohol Depend. 2007;89(2-3):227-33. doi: 
10.1016/j.drugalcdep.2007.01.002. 
101. 
MacKillop J, Amlung MT, Few LR, Ray LA, Sweet LH, Munafo MR. Delayed reward 
discounting and addictive behavior: a meta-analysis. Psychopharmacology. 2011;216(3):305-21. doi: 
10.1007/s00213-011-2229-0. PubMed PMID: 21373791; PubMed Central PMCID: PMCPMC3201846. 
102. 
Hughes JR, Hatsukami D. Signs and Symptoms of Tobacco Withdrawal. Archives of general 
psychiatry. 1986;43(3):289. doi: 10.1001/archpsyc.1986.01800030107013. 
103. 
Hughes JR. Tobacco withdrawal in self-quitters. Journal of Consulting and Clinical Psychology. 
1992;60(5):689-97. doi: 10.1037/0022-006x.60.5.689. 
104. 
Toll BA, O'Malley SS, McKee SA, Salovey P, Krishnan-Sarin S. Confirmatory factor analysis of 
the Minnesota Nicotine Withdrawal Scale. Psychology of addictive behaviors : journal of the Society of 
Psychologists in Addictive Behaviors. 2007;21(2):216-25. doi: 10.1037/0893-164X.21.2.216. PubMed 
PMID: 17563141; PubMed Central PMCID: PMCPMC2527730. 

Protocol version: 4.4 
Protocol date: 06/10/2022 
30 
105. 
Shiffman S, West R, Gilbert D, Craving SWGotAo, Withdrawal in Clinical T. Recommendation 
for the assessment of tobacco craving and withdrawal in smoking cessation trials. Nicotine Tob Res. 
2004;6(4):599-614. doi: 10.1080/14622200410001734067. PubMed PMID: 15370156. 
106. 
Mishory A, Molnar C, Koola J, Li X, Kozel FA, Myrick H, Stroud Z, Nahas Z, George MS. The 
maximum-likelihood strategy for determining transcranial magnetic stimulation motor threshold, using 
parameter estimation by sequential testing is faster than conventional methods with similar precision. The 
journal of ECT. 2004;20(3):160-5. 
107. 
Borckardt JJ, Nahas Z, Koola J, George MS. Estimating resting motor thresholds in transcranial 
magnetic stimulation research and practice: a computer simulation evaluation of best methods. The 
journal of ECT. 2006;22(3):169-75. doi: 10.1097/01.yct.0000235923.52741.72. PubMed PMID: 
16957531. 
108. 
Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, 
Bannister PR, De Luca M, Drobnjak I, Flitney DE, Niazy RK, Saunders J, Vickers J, Zhang Y, De 
Stefano N, Brady JM, Matthews PM. Advances in functional and structural MR image analysis and 
implementation as FSL. NeuroImage. 2004;23 Suppl 1:S208-19. doi: 10.1016/j.neuroimage.2004.07.051. 
PubMed PMID: 15501092. 
109. 
Tziortzi AC, Haber SN, Searle GE, Tsoumpas C, Long CJ, Shotbolt P, Douaud G, Jbabdi S, 
Behrens TEJ, Rabiner EA, Jenkinson M, Gunn RN. Connectivity-Based Functional Analysis of 
Dopamine Release in the Striatum Using Diffusion-Weighted MRI and Positron Emission Tomography. 
Cerebral cortex. 2014;24(5):1165--77. doi: 10.1093/cercor/bhs397. PubMed PMID: Tziortzi2014. 
110.  
Ashare RL, Wileyto EP, Perkins KA, Schnoll RA. The first seven days of a quit attempt predicts 
relapse: Validation of a measure for screening medications for nicotine dependence. Journal of addiction 
medicine. 2013 Jul;7(4):249. 
111.  
Kearney-Ramos TE, Dowdle LT, Lench DH, Mithoefer OJ, Devries WH, George MS, Anton RF, 
Hanlon CA. Transdiagnostic effects of ventromedial prefrontal cortex transcranial magnetic stimulation 
on cue reactivity. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. 2018 Jul 1;3(7):599-
609.