Insular Inhibitory Neuromodulation to Reduce Cigarette Craving and Alter Brain Function in Smokers
Phase: NA
Conditions: Smoking
Sponsor: University of Colorado, Denver

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w 
COMIRB Protocol 
COLORADO MULTIPLE INSTITUTIONAL REVIEW BOARD 
CAMPUS BOX F-490    TELEPHONE:  303-724-1055  Fax:  303-724-0990 
 
 
 
Protocol #: 
15-1016 
Project Title: 
Insular inhibitory neuromodulation to reduce cigarette craving 
and alter brain function in smokers 
 
 
 
Principal Investigator:  
Michael F. Regner, MD 
Version Date: 
Version 3 (August 22, 2018) 
 
 
 
 
  
 
 
I. Hypotheses and Specific Aims 
 
The central hypothesis is that inhibitory insular TMS can be used to provide a non-invasive 
intervention to decrease nicotine craving, alter resting state brain connectivity, and reduce craving-
associated insular activity in smokers. All experiments will be conducted in treatment-seeking 
smokers. These experiments are designed to evaluate the efficacy of insular neuromodulation 
using rTMS. Outcome measures include (1) cigarette craving measures, (2) resting-state fMRI 
connectivity measures, and (3) tasked-based fMRI insular activity measures. In a double-blinded 
two-arm (sham rTMS and inhibitory insular rTMS) randomized control trial with 20 smokers 
assigned to each arm, we will collect craving measurements, resting state fMRI, and task-based 
fMRI before and after a single session of rTMS treatment to investigate the following aims: 
 
AIM 1. Determine if insular rTMS reduces cigarette craving. 
H1 
Compared to sham, insular rTMS will reduce craving in response to cigarette cues. 
 
AIM 2. Determine if insular rTMS alters insular connectivity.  
H2 
Compared to sham, insular rTMS will reduce fMRI connectivity between the insula and 
brain regions associated with executive control, reward, and habit formation.  
 
AIM 3. Determine if insular rTMS alters insular activity.  
H3 
Compared to sham, insular rTMS will reduce fMRI insular activity associated with 
cigarette cues. 
 
 
II. Background and Significance 
 
Nicotine dependence is a major health problem: Smoking is the single greatest preventable 
cause of mortality, morbidity, and unnecessary health care costs in the US.1 It is estimated to 
cause 443,595 deaths annually and $193 billion per year in smoking-related health care costs and 
productivity losses.1 Globally, tobacco results in over 6 million annual deaths and over half a trillion 
dollars in economic damage.2 
 
Current treatments of nicotine dependence are inadequate: Nicotine dependence is notoriously 
difficult to treat, with relapse rates approximately 85% for counseling therapy and 78% for 
combined counseling and pharmacologic therapy.3 Long-term abstinence from smoking is 
uncommon, with 6 month abstinence rates ranging between 19% and 33%.4 High levels of craving 
have been repeatedly shown to be associated with high relapse rates.5–7 Development of novel 
therapies to target craving could dramatically improve abstinence rates, decrease healthcare costs, 
and prolong lives. 
 

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The insula plays a key role in nicotine dependence: The insula is involved in interoception and 
salience. It integrates external information about stimuli and internal sensations including cognitive, 
homeostatic, and emotional states, to guide behavior.8 The anterior insula has been shown to 
exhibit functional connectivity with the superior, middle, and inferior frontal gyri, bilateral 
temporoparietal junction, anterior cingulate cortex, precuneus, basal ganglia, nucleus accumbens, 
and thalamus.8,9 Directed information flow from the insula to other brain regions, visible by fMRI, is 
increased in abstinent compared to satiated states in heavy smokers.10 Moreover, the anterior 
insula shows increased connectivity during smoking cues compared to food cues with 
somatosensory cortex, amygdala, and basal ganglia, which increases as dependence severity 
increases.11 A large meta-analysis demonstrated correlation between anterior insular cue-reactivity 
and subjective craving.12 The anterior insula is also involved in aspects of nicotine dependence 
other than craving, such as delayed discounting,13 valence,14 and mitigating aversion to negative 
consequences of smoking.15 
Several lines of evidence suggest involvement of the anterior insula in maintenance of nicotine 
addiction.16,17 In a landmark study, Naqvi et al16 reported that smokers with cerebrovascular 
damage to the insula were able to stop smoking easily without cravings or relapse, supporting a 
role of the insula and a broader interoception circuit in addiction. A prospective study over a one-
year timespan replicated the conclusion that insular lesions were strongly associated with smokers 
spontaneously becoming non-smokers.18 These findings have been corroborated in rat models of 
nicotine dependence.19 Insular inactivation significantly reduced nicotine motivation, nicotine 
seeking-, and nicotine taking-behaviors, with no effect on food behaviors.19 Results of bilateral 
insular deep brain stimulation lesions in animal models are also consistent with this hypothesis.20 
Given this evidence, inhibiting the anterior insula may be a useful strategy to reduce cigarette 
craving, as will be studied in Aim #1. 
Sutherland et al.21 recently proposed an elegant explanatory “toggle” hypothesis, applying the 
work by Seeley et al.22 to nicotine dependence. Briefly, the hypothesis centers upon the salience 
network, comprised of the insula and anterior cingulate cortex. The insula is believed to “toggle” 
this network, directing brain function towards endogenous (i.e., default mode network) or 
exogenous (i.e., executive control network) stimuli. This insular function becomes usurped in 
nicotine addiction. The concept that the insula directs consciousness towards the most 
homeostatically relevant stimuli – internal or external – provides a neurobiological model to explain 
the cognitive deficits and functional connectivity findings of acute nicotine ingestion, nicotine satiety 
in dependence, and nicotine withdrawal.21 It also provides a mechanism for the findings of 
spontaneous smoking cessation in patients with insular lesions. Importantly, the model affords the 
opportunity to investigate non-invasive perturbation of specific nodes (e.g., insular cortex) to reduce 
craving and hopefully improve abstinence rates. We plan to study this functional perturbation in 
Aim #2 and Aim #3. 
 
Neuromodulation in nicotine dependence shows promising results: Repetitive transcranial 
magnetic stimulation (rTMS) is a non-invasive neuromodulatory technique in which a pulsatile 
magnetic field is created using an external solenoid to induce targeted depolarization of cortical 
neurons via electromagnetic induction. High frequency rTMS (> 3 Hz) transiently increases cortical 
excitability, while low frequency rTMS (1 Hz) reduces it.23–25 TMS to the dorsolateral prefrontal 
cortex (DLPFC) has demonstrated good test-retest reliability in fMRI connectivity.26 
Several studies have reported that excitatory rTMS targeting the DLPFC reduces cigarette 
craving and cigarette consumption.27–29 Moreover, studies have demonstrated that a single session 
of excitatory DLPFC rTMS causes short-term reduction of cigarette craving,30,31 cigarette 
consumption,32 cocaine craving,33 and craving induced by appetitive food.34,35 These findings are 
consistent with the salience “toggling” hypothesis. Prefrontal cortex exerts top-down control to bias 
information in lower-level somatosensory cortices towards relevant input36,37 and guide goal-
directed behavior.38,39 Enhancing prefrontal cortical activity through neuromodulation would be 
expected to shift the balance of neural circuitry away from the DMN and towards the ECN, biasing 
consciousness away from endogenous stimuli such as craving. 
While previous work has reported that TMS targeting the dorsolateral prefrontal cortex (DLPFC) 
modulates craving responses;28,29 human stroke and animal model literature suggests that the 

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insula may be more important in “toggling” away from DMN and toward ECN. To date only one 
paper describes rTMS specifically targeting the insula: a study of seven patients, reporting 
subjective changes in cold perception and tolerance of the intervention.40 Dinur Klein et al.41 
applied high-frequency, low-frequency, and sham rTMS to the bilateral DLPFC with some magnetic 
field spread into the anterior insulae bilaterally. They reported high-frequency bilateral stimulation 
significantly reduced cigarette consumption and nicotine dependence, with significantly increased 
levels of abstinence both immediately after the treatment and at 6 months follow-up. No fMRI data 
were obtained. The major weakness of the Dinur-Klein et al study is that it primarily targeted the 
DLPFC, with minimal excitatory posterior treatment coverage of the insula. Given the salience 
toggling hypothesis, to optimally modulate craving therapies one should provide either excitatory 
stimulation to the DLPFC or inhibitory stimulation of the insula. While most experts in this field 
acknowledge the critical role of the insula in nicotine dependence, there has been surprisingly little 
research exploring neuromodulation of the insula as a potential therapy.  
This proposal seeks to answer several questions: (1) Can inhibitory TMS be used to reduce 
cigarette craving in smokers? (2) Can inhibitory insular TMS alter insular connectivity and activity? 
And, (3) does inhibitory insular TMS alter brain-behavior relationships? The literature implicating 
the insula in maintenance of craving in smokers suggest rTMS targeting the insula may provide a 
more robust modulation of nicotine craving. 
 
 
III. Preliminary Studies/Progress Report  
 
Preliminary study of TMS targeting the 
insula: We measured resting-state fMRI 
connectivity in a healthy subject before 
and after inhibitory (1 Hz) targeting the 
anterior left insula (MNI coordinates: [-32, 
18, 4])9 at 80% motor threshold using a 
custom manufactured MagStim H-coil for 
deeper targeting and Brainsight frameless 
stereotactic TMS localization (Figure 1). 
This approach is identical to the proposed 
neuromodulation. The subject underwent 
a 6 minute resting state fMRI (RS-fMRI) 
exam 
with 
eyes 
closed 
prior 
to 
stimulation. 
The 
subject 
exited 
the 
magnet and was treated with a 15 minute 
continuous train of 1 Hz TMS targeting 
the left anterior insula using an H-coil. 
After stimulation, the subject underwent 
another 6 minute RS-fMRI exam. After 
standard preprocessing, a 5mm radius 
spherical ROI was centered at the TMS 
target 
coordinates, 
and 
connectivity 
measures with all other voxels in the 
brain were calculated. Results were 
thresholded at family-wise p<0.05 using with a voxel-level p<0.005 and a voxel cluster correction 
q>23. Results showed post-treatment increased insular connectivity to DMN regions (cingulate 
cortex and bilateral parietal lobes), middle temporal cortex, and inferior frontal cortex. Decreased 
insular connectivity was observed in amygdala, hippocampus, and dorsal attention areas. These 
results are consistent with our hypotheses and demonstrate (1) we are able to successfully 
administer TMS and measure fMRI connectivity changes, and (2) proof-of-feasibility that TMS 
targeting the insula can modulate neural functional connectivity in the resting state.  
 
Figure 1. Increased (blue) and decreased (red) insular
connectivity after inhibitory rTMS applied to a single
subject. Figure rendered in neurologic space. 

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Figure 3. Neuronal response (n=25) to visual 
stimuli of foods of high hedonic value as 
compared to non-food stimuli, demonstrating 
robust cue-induced activation in the insulae. 
Preliminary study of functional connectivity changes in substance dependence: As part of 
Dr. Tanabe’s (faculty sponsor) research on long term abstinent substance dependent individuals 
(SDI), I investigated RS-fMRI network connectivity between substance dependent individuals (SDI) 
and healthy controls (Figure 2). These SDI are unique in that they had prolonged remission of >1 
year on average. Group independent component analysis was performed on 50 SDI and 50 healthy 
controls, and the number, direction, and strength of connections between components identified as 
standard resting-state networks were analyzed using Granger causality. Compared to controls, SDI 
showed greater Granger causal connectivity from right executive control network (RECN) to dorsal 
default mode network (dDMN) and from dDMN to basal ganglia network (BGN). Interestingly, the 
insula was incorporated in the BGN networks. We concluded that directed effective connectivity 
from the RECN to dDMN and dDMN to BGN in long term abstinent SDI may reflect higher top-
down control from structures involved in executive function and behavioral monitoring over those 
involved in self-referential thought and reward. I presented these data at the International 
Neuropsychological Society 2015 Meeting. These results demonstrate our ability to conduct RS-
fMRI network connectivity analyses which we plan to implement in secondary analyses, in addition 
to the primary seed-based methods shown above (Figure 2). 
 
Preliminary study of neuronal response to 
appetitive food cues compared to neutral cues 
(Tregellas et al. 2013): Dr. Tregellas (co-sponsor) 
has extensive experience measuring the neuronal 
response to visual stimuli of foods of high hedonic 
value 
as 
compared 
to 
non-food 
stimuli,42 
demonstrating robust cue-induced activation in the 
insulae (Figure 3). I plan to use an identical visual 
cue paradigm, but will replace the food-cue images 
with images from the international smoking image 
series.43 
 
 
 
 
 
 
 
 
 
 
 
Figure 2. Left panels, directed Granger causal matrices for SDI (n=50) and controls (n=50). Colorbar corresponds to logarithm of F 
values. Right panels, network density topological graphs of SDI and controls. Thickness of each line corresponds to the Granger 
causal strength and color corresponds to the efferent network.   

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IV. Research Methods 
 
A.  Outcome Measure(s): Outcome measures will include (1) cigarette craving measures, (2) 
resting-state fMRI connectivity measures, and (3) tasked-based fMRI insular activity measures. 
 
A.1 Neuropsychological Testing: Our primary cognitive outcome will be the change after treatment 
compared to before treatment in the Questionnaire of Smoking Urges – Brief Version (QSU-brief), 
a measures craving. An additional important question is whether craving, smoking history, smoking 
characteristics, or personality traits are associated with specific changes in connectivity after 
treatment.  Thus, we will perform the following assessments prior to treatment: (1) smoking history 
questionnaire, (2) Fagerstrom nicotine dependence scale (FNDS), (3) Wisconsin inventory of 
smoking dependence motives (WISDM-68), (4) behavioral inhibitory scale and behavioral 
activation scale (BIS/BAS), (5) Barratt impulsiveness scale (BIS-11). The QSU-brief will be 
administered before and after treatment.  A visual analog scale will be used to quickly assess 
subjects’ craving during the craving visual-cue task: 
 
 
 
The craving data before and after treatment will be used to investigate Aim #1. 
 
A.2.  Magnetic Resonance Imaging. All MRI scans will be acquired using a 3.0 T Seimens Skyra 
MR system using a 20-channel head-neck coil. High-resolution 3D T1-weighted scans will be 
acquired before treatment for image registration and normalization. T2* weighted echo-planar 
imaging blood oxygen level dependent fMRI will be acquired with 3.4 x 3.4 x 3.4 mm voxels, TR 
2100 ms, TE 30 ms, and flip angle 70°. Two fMRI sequences will be acquired before and after 
treatment: (1) a 10 minute RS-fMRI, in which subjects will be asked to lie quietly in the scanner and 
not think of anything in particular; and, (2) a 15 minute task-based fMRI exam during a cigarette 
craving cue task. fMRI data will be processed and analyzed as described in the methods section 
(see C.4 through C.8).  Briefly, outcome measures will include insular functional connectivity (see 
Aim #2) and insular activity (see Aim #3) changes at the whole brain level after treatment 
compared to before treatment. 
 
B. Description of Population to be Enrolled:   
 
Sample Population Recruitment: Current smokers reporting motivation to quit will be recruited 
from the University of Colorado Behavioral Health & Wellness Program (BHWP). BHWP in 
conjunction with the University of Colorado Hospital provides consultation and treatment of tobacco 
dependence through their Tobacco Free Program. Subjects for this study will be recruited from the 
pool of patients considering enrolling in treatment through BHWP at the University of Colorado 
Hospital. Dr. Chad Morris is the director of BHWP; he or his clinical colleagues will ask each patient 
being seen by the Tobacco Cessation Team if they are interested in participating in a research 
study, and if so, they will be provided a flyer that includes basic information as well as Dr. Regner’s 
contact information. The flyer will instruct interested prospective subjects to call or e-mail Dr. 
Regner to set up an appointment. This appointment will allow Dr. Regner to discuss the study with 
the participant in a private office maintained by the Department of Radiology, answer any questions 
the participant may have, and obtain informed consent. Public advertising for this study will also be 
performed using flyers with pull-off tabs (e.g., downtown public areas, convenience stores with 
permission of the owner) and newspaper/internet ads (e.g., Craig’s List, Denver Post). All 

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advertising will comply with the COMIRB Advertising Components Submission Form without any 
deviation. 
 
Inclusion criteria: 
1) Age 18-55 
2) English proficient 
3) Ability to provide informed consent 
4) Self-reported current average daily cigarette consumption >10/day for at least 1 year 
5) Self-reported motivation to quit smoking 
 
Exclusion criteria:  
1) MRI/TMS44,45 exclusions, including  
a. Claustrophobia 
b. Intracranial or spinal hardware 
c. Pacemakers 
d. MR-incompatible devices (Examples: pacemaker, deep brain stimulator, vagal nerve 
stimulator, cochlear implant,  insulin pump, implanted medication pump, bone 
stimulator, implanted defibrillator) 
e. History of metal objects or fragments in the eye or skull, including shrapnel or metal 
plates 
f. 
History of stroke or other brain lesion 
g. History of attempted suicide or suicidal ideation 
h. Personal history of headaches, seizures, epilepsy, or status epilepticus 
i. 
Family history of epilepsy 
j. 
Medications known to lower seizure threshold (e.g., tricyclic antidepressants, 
antipsychotics, psychostimulants) 
k. Increased intracranial pressure, hydrocephalus, or pseudotumor cerebri 
l. 
Unstable coronary artery disease 
m. Current pregnancy or positive urine pregnancy test 
2) Neurological illness 
3) Prior neurosurgery 
4) Schizophrenia 
5) Bipolar disorder 
6) Current (within the last two months) major depressive disorder 
7) Substance dependence (except cannabis) or positive urinalysis for opiates, stimulants, or 
sedative on the day of testing 
8) Alcohol dependence or positive breath test for alcohol on the day of testing 
9) Use of tobacco products other than cigarettes.  
 
Subjects will be randomized to one of two treatment groups: 1) sham TMS, or 2) inhibitory (1 Hz) 
TMS targeting the insula. 
 
C. Study Design and Research Methods   
 
Experimental Method for all Aims:  
 
C.1 Enrollment. Prior to informed consent, the inclusion and exclusion criteria will be reviewed with 
each subject. Once eligibility has been determined, each subject will be provided with verbal and 
written informed consent.  All questions will be answered. 
Each subject will participate in a single session, during which inclusion and exclusion criteria will 
be reviewed, the subject will provide informed consent, women will be have urine pregnancy 
screens, urine and breath drug and alcohol tests will be administered, standardized psychological 
assessments will be performed, and then resting and task-based fMRI scans before and after 
rTMS. The following is the projected timeline for a given subject’s study appointment: 
 

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Study Appointment Component 
Duration (min) 
1. Review of inclusion and exclusion criteria 
10 
2. Informed Consent 
15 
3. Urine and breath tests 
5 
4. Pre-treatment neurological examination 
10 
5. Initial standardized surveys 
20 
6. Pre-treatment MRI examination 
49 
               High- definition structural T1W sequence 
6 
               Resting-state fMRI sequence 
10 
               Craving-cue task fMRI sequence/task 
15 
               Magnetic Resonance Spectroscopy (MRS) 
18 
7. Walk to TMS Laboratory 
5 
8. rTMS Treatment 
31 
9. Walk to Brain Imaging Center 
5 
10. Post-treatment MRI examination 
43 
               Resting-state fMRI sequence 
10 
               Craving-cue task fMRI sequence/task 
15 
               Magnetic Resonance Spectroscopy (MRS) 
18 
11. Post-treatment QSU-Brief 
2 
12. Post-treatment neurological examination 
10 
Total: 
205 
 
Subjects will be asked to remain abstinent from smoking for 3 hours prior to the study. Subjects 
will provide urine samples to screen for recent drug use and quantify urine cotinine levels. To 
ensure abstinence they must fill a questionnaire, test negative for alcohol by a breath screening 
test, and have a breath CO<10pp. Clinical assessment including a brief medical and psychiatric 
interview will be performed by Dr. Regner to determine eligibility. Subjects will undergo a brief (10 
minute) neurological examination. Subjects will be educated on TMS and MR safety and will 
demonstrate their understanding. 
 
C.2 Demographic, Psychological, Smoking, and Craving Assessments: Subjects will complete a 
basic demographic survey to document the date of birth, sex, ethnicity, education, handedness, 
and use of glasses. Age and sex must be determined because gray matter characteristics and 
brain function vary by age. Education and handedness must be documented because brain 
function is affected by these variables. The use of glasses must be documented because if 
subjects are unable to see the images during the craving cue task, it can introduce a bias in the 
data. 
Medical history, current medications, and surgical/procedural history will be documented.  Dr. 
Regner will review this information to ensure that the subject meets no exclusion criteria, and to 
ensure that the subject is taking no medications that lower the seizure threshold. 
Subjects will complete a brief smoking history questionnaire.  This will characterize the severity 
and nature of the subject’s cigarette dependence, which may affect their responses to craving 
cues. 
Subjects will complete the Fagerstrom Nicotine Dependence Scale (FNDS).  This is a valid and 
reliable standardized survey to quantify the degree of cigarette dependence.46,47 
Subjects will complete the Wisconsin Inventory of Smoking Dependence Motives (WISDM-68).  
This is a valid and reliable 68-item standardized survey designed to characterize the psychological 
constructs that motive an individual subject’s smoking behaviors.48 
Subjects will complete the Questionnaire on Smoking Urges – Brief (QSU-Brief).  This is a valid 
and reliable standardized survey to quantify a subject’s current intensity of craving.49  This is the 
only standardized survey that in addition to being administered prior to rTMS treatment will also be 
administered after rTMS treatment. 
Subjects will complete the Behavioral Inhibition Scale and Behavioral Activation Scale 
(BIS/BAS).  The Behavioral Inhibition and Activation Scale is a 20-item self-reported questionnaire 

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used to measure responsiveness of motivational systems.50,51 The first component is the 
Behavioral Inhibition Scale, which is used to quantify avoidance behaviors and reactions to the 
anticipation of punishment.  The second component is the Behavioral Activation Scale, which is 
used to quantify positive affective and approach response tendencies to appetitive stimuli. 
Lastly, subjects will complete the Barratt Impulsiveness Scale, version 11 (BIS-11).  This is a 
30-item self-reported questionnaire used to measure impulsivity; subjects rated whether phrases 
and words describing aspects of impulsivity are self-descriptive.52 
 
C.2 MRI scan acquisition parameters: Prior to the MRI scan, subjects will be asked to complete the 
standard UC Denver Brain Imaging Center MRI Research Subject Screening Form to ensure they 
are safe to be scanned. Before and after rTMS, RS-fMRI and task-based fMRI examinations will be 
performed. This is a required component of any MRI examination. All scans will be performed on a 
research-dedicated 3.0 T Seimens Skyra MR system using a 20-channel head-neck coil. A VacFix 
head-confirming vacuum cushion will mitigate effects of head motion. High-resolution 3D T1-
weighted scans will be acquired before treatment for image registration and normalization. Linear 
and second order shimming will be performed to reduce static field inhomogeneities at the skull 
base prior to fMRI scans. T2* weighted echo-planar imaging blood oxygen level dependent fMRI 
will be acquired with 3.4 x 3.4 x 3.4 mm voxels, TR 2100ms, TE 30ms, and flip angle 70°. Two 
fMRI exams will be acquired before and after treatment: (1) a 10 minute RS-fMRI, in which subjects 
will be asked to lay quietly in the scanner and not think of anything in particular; and, (2) a 15 
minute task-based fMRI exam during a cigarette craving cue task.  
 
C.3 1H-Magnetic Resonance Spectroscopy (MRS): Before and after rTMS, 1H-MRS examinations 
will be performed to quantify GABA, Glutamate, and Glutamine levels in the insula. 1H-MRS 
sequences will be used, as previously reported and used by colleagues in the Department of 
Radiology.53 This MR sequence will be used for exploratory analysis. 
 
C.3 rTMS Treatment: Subjects in 
the insular rTMS treatment arm will 
undergo a single 25 minute train of 
1Hz rTMS targeting the right 
anterior 
insula16,17,54 
(MNI 
coordinates: [32, 18, 4])9 at 90% 
motor 
threshold 
using 
the 
commercially available MagStim 
Rapid2 
coil 
and 
Brainsight 
frameless 
stereotactic 
TMS 
localization system. Subjects in the 
sham rTMS treatment arm will 
have a sham coil placed in the 
same position. The sham coil 
creates sound, vibrations, and electrical stimulation similar to the active coil.55  Subjects will 
undergo a brief (10 minute) neurological examination by Dr. Regner before and after rTMS 
treatment, which will include examination of the motor, sensory, language, abstract-thinking, 
memory, and spatial reasoning systems. 
 
The specific parameters of the rTMS proposed in this study are as follows: 
 
Parameter 
Value 
Manufacturer 
MagnaStim 
Model Name 
Rapid2 
510(k) or Master File Number 
 
Amplitude (% of motor threshold) 
90% 
Frequency (pulses per second)   [a] 
1 Hz 
Figure 4. Timeline of an individual subject’s study day. 

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Train duration    [b] 
1500 sec (25 min) 
Number of trains per session    [c] 
1 
Number of sessions in study    [d] 
1 
Cumulative exposure (total # of pulses = a × b × c × d) 
1500 pulses 
Intertrain interval (time between trains) 
N/A 
 
The following table is the maximum safe duration (in seconds) of a single train of rTMS, reproduced 
without permission from Rossi et al.45  Here, safety was defined as absence of seizure, spread of 
excitation or afterdischarge of EMG activity.  Numbers preceded by > are the longest duration 
tested.  This table was presented by consensus.45  The parameters proposed in our study are 
highlighted in yellow and well within safe limits. 
 
Freq (Hz) 
Intensity (% of motor threshold) 
90% 
100% 
110% 
120% 
130% 
1 
>1800 
>1800 
>1800 
>360 
>50 
5 
>10 
>10 
>10 
>10 
>10 
10 
>5 
>5 
>5 
4.2 
2.9 
20 
2.05 
2.05 
1.6 
1.0 
0.55 
25 
1.28 
1.28 
0.84 
0.4 
0.24 
 
C.4 Comparison of craving responses to treatment: QSU and visual-analogue craving measures 
will be compared using a multivariate analysis of covariance (see C.4.1.1). These comparisons will 
provide results for Aim #1. 
 
C.5 Image preprocessing: Raw BOLD data will be pre-processed using SPM8 (website: 
http://www.fil.ion.ucl.ac.uk). After discarding the first four volumes for saturation effects, motion 
correction will be applied with a 3D rigid body transformation. Subjects with head movement >2mm 
will be excluded. fMRI images will be spatially normalized to Montreal Neurological Institute (MNI) 
space by registering each subject’s fMRI images to their high resolution T1W scan, which will be 
registered to MNI space using the DARTEL method.56 Registered fMRI images will be spatially 
smoothed using a 4mm FWHM isotropic Gaussian kernel. Band-pass filtering (0.01Hz–0.08Hz) will 
be applied to reduce effects of low frequency drift and high-frequency physiologic noise. fMRI data 
will be corrected for movement parameters (3 rotational and 3 translational variables) as well as 
global, CSF, and white matter signal fluctuations.9,57 
 
C.6 Seed Based Insular Functional Connectivity: Using customized MATLAB scripts, whole brain 
voxel-wise connectivity maps will be calculated according to Margulies et al.58 from the 10 minute 
RS-fMRI exams using a seed-based approach with a 5mm radius sphere centered at the right 
anterior insular rTMS target. To compare insular connectivity, first we will apply a fixed-effects 
analysis to each subject’s resultant whole-brain insular connectivity maps to investigate individual 
brain connectivity differences associated with treatment. Treatment and group comparisons will be 
performed using ANCOVA. These comparisons will provide results for Aim #2. 
 
C.7 Craving cue task-based insular activity: After resting state fMRI examinations, all subjects will 
perform smoking cue/neutral cue craving tasks during a 15 minute fMRI using the aforementioned 
imaging parameters. We have previous experience using this task to elicit food craving.42 We will 
use an identical visual cue paradigm, but will replace the food-cue images with the international 
smoking image series.43 Each run will consist of pseudorandom presentations of different smoking-
related and food-related images. Between images, participants will be asked to fixate on a cross 
image for variable duration of time to introduce jitter into the design and improve fit of the 
generalized linear model (GLM). 
Task-based fMRI exams will be preprocessed as above. Data will be modeled in SPM8 using a 
GLM with customized square waveforms representing the condition (i.e., cigarette versus neutral 
stimulus) and duration of stimulus convolved with a canonical hemodynamic response function. 

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Contrast maps of smoking>neutral cues will be analyzed using second-level random-effects to 
compare these contrasts within and between treatment groups using ANCOVA. Two separate task-
based analyses will be performed: (1) insular ROI analyses by extracting insular activity from the 
fMRI with a 5mm radius sphere centered at the insular TMS target, and (2) whole brain analysis. 
Both analyses will control for multiple comparisons using cluster-based correction for family-wise 
error p<0.05. These comparisons will provide results for Aim #3. 
 
C.8 Resting state network analysis (secondary): One inherent limitation of the seed-based 
connectivity approach is that the resultant connectivity maps are highly dependent upon ROI 
selection. To circumvent the potential interference caused by seed selection, secondary 
investigation of the functional connectome will be performed using group independent component 
analysis59 to make inferences using the GIFT toolbox (http://icatb.sourceforge.net) and using the 
same method and analysis pipeline as our preliminary data. Resultant resting state network 
connectivity maps will be compared by group to investigate differences in strength and direction of 
connectivity between salience, default mode, and executive control networks. 
 
C.9 Brain-behavior relationships (secondary): Secondary analysis of brain-behavior relationships 
will be conducted using a GLM in SPM8 by regressing insular connectivity and activity (see C.3.6) 
against craving and dependence measures. This will characterize the relationship between 
behavior (Aim #1) and brain function (insular connectivity [Aim #2] and activity [Aim #3]). 
 
C.10 Relative concentrations of GABA, glutamate, and glutamine after rTMS (exploratory):  
Exploratory analysis of the changes in GABA, glutamate, and glutamine concentrations induced by 
rTMS will be performed using spectroscopic peak detection methods and ANCOVA. 
  
D.   Description, Risks and Justification of Procedures and Data Collection Tools: 
 
Risks Associated with TMS 
1. Headaches (common).  The risk of headaches or scalp discomfort is approximately 10%.  
This discomfort is self-limited and responds to over the counter analgesics (e.g., ibuprofen, 
aspirin, or acetaminophen). 
2. Seizures (rare). When used within parameters set by recent consensus statements, the 
risk of seizures is less than 1 in 1,000.  Seizures reported have been self-limited.  The 
lifetime incidence of seizures in the US population is 10%.  Isolated self-limited seizures do 
not cause brain injury or other adverse effects to the subject, are painless, and do not have 
known adverse effects on health.  There have been no reported cases of status epilepticus 
(uncontrollable seizure which can be disabling or life-threatening) or epilepsy (recurrent, 
unprovoked seizures) following TMS.  When evaluating subject risk per the standardized 
COMIRB review procedure for TMS studies, studies with a low (<1%) risk of a single self-
limited seizure should not be considered a serious risk to the health of a subject.  In 
addition, according to the COMIRB Guidelines for TMS Protocol Evaluation, studies with 
stimulation parameters within the Wasserman criteria60 and low frequency (≤1 Hz)45 are 
likely safe without need for IDE or further FDA evaluation.  Because this study will use a 
low frequency single train rTMS (1 Hz) within Wasserman criteria, ensure adequate 
hearing protection, administer neurological examinations before and after rTMS treatment, 
and monitor subjects by a medical doctor (Dr. Regner) during and for at least 60 minutes 
after rTMS treatment, we do not feel that an FDA IDE is required.  This opinion is 
consistent with the aforementioned guidelines and COMIRB TMS review guidelines (our 
decision pathway is highlighted in blue): 

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Dr. Regner will be present throughout the subject’s participation and for 60 minutes after 
the completion of the TMS treatment (during MRI scans). He will monitor for signs of 
seizure or muscle twitching.  Dr. Regner is a physician trained in seizure management and 
will have ready access to life-support equipment (oxygen, suction, blood pressure 
monitoring, CPR equipment) and antiepileptic drugs. 
3. Hearing loss (rare). The risk of hearing loss is well below 1 in 10,000 and appears to be 
completely avoidable with appropriate hearing protection (ear plugs). All subjects are 
required to wear the standard-of-care hearing protection. Subjects are asked to report to 
study staff immediately if their hearing protection becomes loose or is otherwise 
inadequate. In addition, study staff will monitor all subjects to ensure compliance with 
hearing protection. 
4. Movement or heating of metallic objects (rare). TMS may cause heating or movement 
of intracranial metal, such as in subjects with implantable electrodes, cerebrospinal fluid 
shunts, skull plates, or other intracranial metal.  These subjects will be excluded. 
5. Damage to electronic medical devices (rare). TMS may interfere with electronic medical 
devices such as pacemakers, implantable stimulators, or implantable pumps. These 
subjects will be excluded. 
6. Pregnancy (rare). There is not sufficient data to assess the safety of TMS in pregnancy, 
although TMS has been performed in limited numbers of pregnant subjects without 
adverse effects.  Pregnant subjects will be excluded. 
 
Risks Associated with MRI 
7. Movement or heating of metallic objects (rare, serious). The only known hazard 
associated with exposure to a static high magnetic field is that the magnet exerts a strong 
force on ferromagnetic objects. For this reason, ferromagnetic objects are excluded from 
the vicinity of the magnet so that they will not become projectiles. At the Brain Imaging 
Center, the research MRI for human use has a field strength of 3.0 T. Imaging at these 
field strengths is not considered a significant risk according to FDA guidelines. The 
scanning sequences applied are within the FDA guidelines for human MR scanning. In 
addition, every subject undergoes an extensive safety screening to determine whether 

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he/she has any implanted materials, objects, device, or dental retainers that may pose a 
risk. If there is any doubt about the nature of any implanted material, or any other 
contraindication to MR scanning, the subject will not be scanned. 
8. Damage to electronic medical devices (rare, serious).  See #7. 
9. Pregnancy (rare, serious). Any woman who reports being pregnant or reports that they 
may be pregnant will be excluded from this study. Any woman of childbearing age who 
reports that she is not pregnant will undergo a urine pregnancy screen. Those who test 
positive will be excluded from participating in this study. 
10. Claustrophobia (common). If patients are anxious they can be trained in the mock 
scanner located adjacent to the MR scanner to mitigate anxiety and help the subject adjust 
to the scanning environment. Subjects who want to quit immediately or who are otherwise 
not interested in participating in this study can do so at any time, and this option will be 
discussed with the subject during informed consent and at regular intervals thereafter. 
11. Nausea (rare). Rarely, subjects may become nauseated as a result of the alternative 
magnetic fields or acoustic noise. Subjects are able to terminate participation in the study 
at any time. 
12. Irritation from loud sounds (common). Loud sounds occur during functional MRI 
scanning. They are unpleasant, but not loud enough to be harmful.  All subjects will wear 
standard-of-care ear protection while undergoing the MRI scans. Subjects are able to 
terminate participation in the study at any time. 
 
General Risks Associated with Research Participation 
13. Potential risk of loss of confidentiality (rare). This study includes drug screening, 
alcohol screening, and pregnancy test assessment. There is the potential that this 
information may not be kept confidential, such as in theft of study material. 
14. Fatigue (common). Tiredness or boredom may occur during behavioral tasks and the MRI 
scan. Subjects are able to terminate participation in the study at any time. 
15. Frustration (common). Subjects may become frustrated while performing the craving-cue 
task in the MRI scanner.  Subjects are able to terminate participation in the study at any 
time. 
    
E.   Potential Scientific Problems:   
 
I have experience conducting both structural and function neuroimaging experiments.61,62 
Nonetheless, this study has potential pitfalls. One limitation is that insular rTMS may also stimulate 
the lateral inferior frontal gyrus; by design, any rTMS treatment can also affect structures superficial 
to the target along the 
 trajectory. This potential pitfall can be easily addressed by correlating 
craving measures with (1) exploratory seed-based connectivity and (2) ROI based activity of all 
structures included in the rTMS field. Such exploration would determine if other structures showed 
altered connectivity/activity after insular rTMS, as well as determine if brain-behavior relationships 
in other structures were altered by insular rTMS. Another potential limitation is that the effect of 
insular rTMS may manifest only as changes in insular connectivity but not insular activity – we 
address this predicament by investigating both. 
 
F.   Data Analysis Plan:   
 
F.1  Aim 1: Determine if insular TMS reduces cigarette craving: 
F.1.1 Statistical Design: For a multivariate analysis of covariance model, we will fit a general 
linear multivariate model, with measures of craving before and after treatment as the outcomes. As 
predictors, we will fit indicator variables for treatment assignment, and FTND.  We will use the 
Hotelling-Lawley test to assess treatment by time interaction, and the main effects of time and 
treatment. 
F.1.2 Sample Size and Power Analysis: Li et al.30 conducted an experiment similar to ours, 
measuring craving responses to DLPFC rTMS (Cohen’s d = 3.0) or sham TMS (Cohen’s d = 1.9). 

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Based on these estimates, we will need to recruit 15 subjects to each arm (total n=30) to detect 
similar differences with 80% power and 0.05 significance level. Our study should achieve similar 
power, even with up to 25% loss to follow-up in each arm. If our loss-to-follow up is only 10% per 
arm, we can detect an effect size as small as 0.962 with 80% power and 0.05 significance level. 
Our sample size is larger or similar to other studies using similar methods.27,29,30,32,63 
 
F.2  Aim 2: Determine if insular TMS alters insular connectivity: 
F.2.1 Statistical Design: A second-level random effects model will be used to compare insular 
connectivity by treatment and group using ANCOVA. Age will be controlled for as nuisance 
variables. A Monte Carlo cluster correction threshold will be applied to correct for multiple 
comparisons, using a voxel-wise p<0.005 and family-wise p < 0.05.  
F.2.2 Sample Size and Power Analysis:  Hanlon et al.26 measured fMRI connectivity changes 
after rTMS to two separate prefrontal targets, resulting in Cohen’s d values greater than 1.9. Based 
on these estimates, our target recruitment goal must be greater than 6 in each arm (total n=12) to 
detect significant differences with 80% power and 0.05 significance level. Our sample size n=32 
(after 10% attrition of fMRI data, 10% attrition of TMS administration) is similar or larger compared 
to other studies using similar methods.26,30,33,64,65 With a sample size of 32, we can detect a 
correlational effect size as small as 0.30 (
) with 80% power and 0.05 significance level. 
 
F.3  Aim 3: Determine if insular TMS alters insular activity: 
F.3.1 Statistical Design: Contrast maps of smoking>neutral cues will be analyzed using second-
level random-effects to compare these contrasts within and between treatment groups using 
ANCOVA. Two separate task-based analyses will be performed: (1) insular ROI analyses by 
extracting insular activity from the fMRI with a 5mm radius sphere centered at the insular TMS 
target, and (2) whole brain analysis. Both analyses will control for multiple comparisons and 
nuisance variables as we will in Aim #2. 
F.3.2 Sample Size and Power Analysis: Hanlon et al.26 measured fMRI activity changes after 
rTMS to two separate prefrontal targets, resulting in Cohen’s d values greater than 2.3. Based on 
these estimates, our target recruitment goal must be greater than 5 in each arm (total n=10) to 
detect significant differences with 80% power and 0.05 significance level. Our sample size n=32 
(after 10% attrition of fMRI data, 10% attrition of TMS administration) is similar or larger compared 
to other studies using similar methods.26,30,33,64,65 With a sample size of 32, we can detect a 
correlational effect size as small as 0.30 (
) with 80% power and 0.05 significance level. 
 
G.  Summarize Knowledge to be Gained:   
 
Regardless of the results of our aims, this proposal will lead to significant future research 
directions. In the unexpected case that no differences in craving or fMRI are observed after insular 
rTMS, our interpretation will be that the insula is not as critical a neural node as the literature to 
date reports. This null result would direct our future efforts toward prefrontal targets, which has 
been previously demonstrated to be effective in craving mitigation. If we find that the insula is 
indeed an important neural node in craving behavior, these preliminary studies would pave the way 
for future, more sophisticated studies that compare and combine insular rTMS with prefrontal 
rTMS, characterize the laterality of the insula, and investigate the durability of craving mitigation 
with repeated rTMS as a potential long-term adjuvant treatment for nicotine dependence. Literature 
to date strongly support the latter conclusion, and we expect to find significant results. Regardless 
of outcome, this proposal will answer a critically important question for the millions of patients 
unable to stop smoking: how important is the insula in cigarette craving, and can insular rTMS 
reduce cigarette craving? 
 
H. References: 
 
1. 
Hays, J. T., Ebbert, J. O. & Sood, A. Treating tobacco dependence in light of the 2008 US 
Department of Health and Human Services clinical practice guideline. Mayo Clin. Proc. 84, 
730–735; quiz 735–736 (2009). 
2. 
WHO. WHO report on the global tobacco epidemic. (World Health Organization, 2013). 

Protocol Template 
Page 14 
CF-146, Effective 7/10/11 
 
3. 
Clinical Practice Guideline Treating Tobacco Use and Dependence 2008 Update Panel, 
Liaisons, and Staff. A clinical practice guideline for treating tobacco use and dependence: 
2008 update. A U.S. Public Health Service report. Am. J. Prev. Med. 35, 158–176 (2008). 
4. 
Fiore, M. Treating tobacco use and dependence; 2008 guideline. (2008). 
5. 
Allen, S. S., Bade, T., Hatsukami, D. & Center, B. Craving, withdrawal, and smoking urges on 
days immediately prior to smoking relapse. Nicotine Tob. Res. Off. J. Soc. Res. Nicotine Tob. 
10, 35–45 (2008). 
6. 
Hughes, J. R. & Hatsukami, D. Signs and symptoms of tobacco withdrawal. Arch. Gen. 
Psychiatry 43, 289–294 (1986). 
7. 
Zhou, X. et al. Attempts to quit smoking and relapse: factors associated with success or failure 
from the ATTEMPT cohort study. Addict. Behav. 34, 365–373 (2009). 
8. 
Cauda, F. et al. Meta-analytic clustering of the insular cortex: characterizing the meta-analytic 
connectivity of the insula when involved in active tasks. NeuroImage 62, 343–355 (2012). 
9. 
Cauda, F. et al. Functional connectivity of the insula in the resting brain. NeuroImage 55, 8–23 
(2011). 
10. Ding, X. & Lee, S.-W. Changes of functional and effective connectivity in smoking 
replenishment on deprived heavy smokers: a resting-state FMRI study. PloS One 8, e59331 
(2013). 
11. Claus, E. D., Blaine, S. K., Filbey, F. M., Mayer, A. R. & Hutchison, K. E. Association between 
nicotine dependence severity, BOLD response to smoking cues, and functional connectivity. 
Neuropsychopharmacol. Off. Publ. Am. Coll. Neuropsychopharmacol. 38, 2363–2372 (2013). 
12. Tang, D. W., Fellows, L. K., Small, D. M. & Dagher, A. Food and drug cues activate similar 
brain regions: a meta-analysis of functional MRI studies. Physiol. Behav. 106, 317–324 (2012). 
13. Clewett, D. et al. Increased functional coupling between the left fronto-parietal network and 
anterior insula predicts steeper delay discounting in smokers. Hum. Brain Mapp. 35, 3774–
3787 (2014). 
14. Chua, H. F., Gonzalez, R., Taylor, S. F., Welsh, R. C. & Liberzon, I. Decision-related loss: 
regret and disappointment. NeuroImage 47, 2031–2040 (2009). 
15. Dinh-Williams, L., Mendrek, A., Bourque, J. & Potvin, S. Where there’s smoke, there’s fire: the 
brain reactivity of chronic smokers when exposed to the negative value of smoking. Prog. 
Neuropsychopharmacol. Biol. Psychiatry 50, 66–73 (2014). 
16. Naqvi, N. H., Rudrauf, D., Damasio, H. & Bechara, A. Damage to the insula disrupts addiction 
to cigarette smoking. Science 315, 531–534 (2007). 
17. Moran-Santa Maria, M. M. et al. Right anterior insula connectivity is important for cue-induced 
craving in nicotine-dependent smokers. Addict. Biol. 20, 407–414 (2015). 
18. Suñer-Soler, R. et al. Smoking cessation 1 year poststroke and damage to the insular cortex. 
Stroke J. Cereb. Circ. 43, 131–136 (2012). 
19. Forget, B., Pushparaj, A. & Le Foll, B. Granular insular cortex inactivation as a novel 
therapeutic strategy for nicotine addiction. Biol. Psychiatry 68, 265–271 (2010). 
20. Pushparaj, A. et al. Electrical stimulation of the insular region attenuates nicotine-taking and 
nicotine-seeking behaviors. Neuropsychopharmacol. Off. Publ. Am. Coll. 
Neuropsychopharmacol. 38, 690–698 (2013). 
21. Sutherland, M. T., McHugh, M. J., Pariyadath, V. & Stein, E. A. Resting state functional 
connectivity in addiction: Lessons learned and a road ahead. NeuroImage 62, 2281–2295 
(2012). 
22. Seeley, W. W. et al. Dissociable intrinsic connectivity networks for salience processing and 
executive control. J. Neurosci. Off. J. Soc. Neurosci. 27, 2349–2356 (2007). 
23. Feil, J. & Zangen, A. Brain stimulation in the study and treatment of addiction. Neurosci. 
Biobehav. Rev. 34, 559–574 (2010). 
24. Hoogendam, J. M., Ramakers, G. M. J. & Di Lazzaro, V. Physiology of repetitive transcranial 
magnetic stimulation of the human brain. Brain Stimulat. 3, 95–118 (2010). 
25. Pell, G. S., Roth, Y. & Zangen, A. Modulation of cortical excitability induced by repetitive 
transcranial magnetic stimulation: Influence of timing and geometrical parameters and 
underlying mechanisms. Prog. Neurobiol. 93, 59–98 (2011). 

Protocol Template 
Page 15 
CF-146, Effective 7/10/11 
 
26. Hanlon, C. A. et al. 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 8, 
e67917 (2013). 
27. 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. Addict. Abingdon Engl. 104, 653–660 (2009). 
28. Hayashi, T., Ko, J. H., Strafella, A. P. & Dagher, A. Dorsolateral prefrontal and orbitofrontal 
cortex interactions during self-control of cigarette craving. Proc. Natl. Acad. Sci. U. S. A. 110, 
4422–4427 (2013). 
29. Jansen, J. M. et al. Effects of non-invasive neurostimulation on craving: a meta-analysis. 
Neurosci. Biobehav. Rev. 37, 2472–2480 (2013). 
30. Li, X. et al. Repetitive transcranial magnetic stimulation of the dorsolateral prefrontal cortex 
reduces nicotine cue craving. Biol. Psychiatry 73, 714–720 (2013). 
31. Johann, M. et al. [Transcranial magnetic stimulation for nicotine dependence]. Psychiatr. Prax. 
30 Suppl 2, S129–131 (2003). 
32. Eichhammer, P. et al. High-frequency repetitive transcranial magnetic stimulation decreases 
cigarette smoking. J. Clin. Psychiatry 64, 951–953 (2003). 
33. Camprodon, J. A., Martínez-Raga, J., Alonso-Alonso, M., Shih, M.-C. & Pascual-Leone, A. 
One session of high frequency repetitive transcranial magnetic stimulation (rTMS) to the right 
prefrontal cortex transiently reduces cocaine craving. Drug Alcohol Depend. 86, 91–94 (2007). 
34. Uher, R. et al. Effect of left prefrontal repetitive transcranial magnetic stimulation on food 
craving. Biol. Psychiatry 58, 840–842 (2005). 
35. Barth, K. S. et al. Food cravings and the effects of left prefrontal repetitive transcranial 
magnetic stimulation using an improved sham condition. Front. Psychiatry 2, 9 (2011). 
36. Egner, T. & Hirsch, J. Cognitive control mechanisms resolve conflict through cortical 
amplification of task-relevant information. Nat. Neurosci. 8, 1784–1790 (2005). 
37. King, J. A., Korb, F. M., von Cramon, D. Y. & Ullsperger, M. Post-error behavioral adjustments 
are facilitated by activation and suppression of task-relevant and task-irrelevant information 
processing. J. Neurosci. Off. J. Soc. Neurosci. 30, 12759–12769 (2010). 
38. Frank, M. J., Woroch, B. S. & Curran, T. Error-related negativity predicts reinforcement 
learning and conflict biases. Neuron 47, 495–501 (2005). 
39. Magno, E., Foxe, J. J., Molholm, S., Robertson, I. H. & Garavan, H. The anterior cingulate and 
error avoidance. J. Neurosci. Off. J. Soc. Neurosci. 26, 4769–4773 (2006). 
40. Ciampi de Andrade, D. et al. Into the island: a new technique of non-invasive cortical 
stimulation of the insula. Neurophysiol. Clin. Clin. Neurophysiol. 42, 363–368 (2012). 
41. Dinur-Klein, L. et al. Smoking cessation induced by deep repetitive transcranial magnetic 
stimulation of the prefrontal and insular cortices: a prospective, randomized controlled trial. 
Biol. Psychiatry 76, 742–749 (2014). 
42. Cornier, M.-A. et al. Differences in the neuronal response to food in obesity-resistant as 
compared to obesity-prone individuals. Physiol. Behav. 110-111, 122–128 (2013). 
43. Gilbert, D. G. & Rabinovich, N. E. International smoking image series (with neutral 
counterparts), version 1.2. Carbondale Integr. Neurosci. Lab. Dep. Psychol. South. Ill. Univ. 
(1999). 
44. Rossi, S., Hallett, M., Rossini, P. M. & Pascual-Leone, A. Screening questionnaire before 
TMS: An update. Clin. Neurophysiol. 122, 1686 (2011). 
45. Rossi, S., Hallett, M., Rossini, P. M. & Pascual-Leone, A. Safety, ethical considerations, and 
application guidelines for the use of transcranial magnetic stimulation in clinical practice and 
research. Clin. Neurophysiol. 120, 2008–2039 (2009). 
46. Heatherton, T. F., Kozlowski, L. T., Frecker, R. C. & Fagerström, K. O. The Fagerström Test 
for Nicotine Dependence: a revision of the Fagerström Tolerance Questionnaire. Br. J. Addict. 
86, 1119–1127 (1991). 
47. Etter, J. F., Duc, T. V. & Perneger, T. V. Validity of the Fagerström test for nicotine 
dependence and of the Heaviness of Smoking Index among relatively light smokers. Addict. 
Abingdon Engl. 94, 269–281 (1999). 

Protocol Template 
Page 16 
CF-146, Effective 7/10/11 
 
48. Piper, M. E. et al. A multiple motives approach to tobacco dependence: the Wisconsin 
Inventory of Smoking Dependence Motives (WISDM-68). J. Consult. Clin. Psychol. 72, 139–
154 (2004). 
49. Cappelleri, J. C. et al. Multivariate framework of the Brief Questionnaire of Smoking Urges. 
Drug Alcohol Depend. 90, 234–242 (2007). 
50. Carver, C. S. & White, T. L. Behavioral inhibition, behavioral activation, and affective 
responses to impending reward and punishment: the BIS/BAS scales. J. Pers. Soc. Psychol. 
67, 319 (1994). 
51. Campbell-Sills, L., Liverant, G. I. & Brown, T. A. Psychometric evaluation of the behavioral 
inhibition/behavioral activation scales in a large sample of outpatients with anxiety and mood 
disorders. Psychol. Assess. 16, 244 (2004). 
52. Patton, J. H., Stanford, M. S. & Barratt, E. S. Factor structure of the Barratt impulsiveness 
scale. J. Clin. Psychol. 51, 768–774 (1995). 
53. Rojas, D. C., Singel, D., Steinmetz, S., Hepburn, S. & Brown, M. S. Decreased left perisylvian 
GABA concentration in children with autism and unaffected siblings. NeuroImage 86, 28–34 
(2014). 
54. Kühn, S. & Gallinat, J. Common biology of craving across legal and illegal drugs - a 
quantitative meta-analysis of cue-reactivity brain response. Eur. J. Neurosci. 33, 1318–1326 
(2011). 
55. Triggs, W. J. et al. Right and left dorsolateral pre-frontal rTMS treatment of refractory 
depression: a randomized, sham-controlled trial. Psychiatry Res. 178, 467–474 (2010). 
56. Ashburner, J. A fast diffeomorphic image registration algorithm. NeuroImage 38, 95–113 
(2007). 
57. Braun, U. et al. Test-retest reliability of resting-state connectivity network characteristics using 
fMRI and graph theoretical measures. NeuroImage 59, 1404–1412 (2012). 
58. Margulies, D. S. et al. Mapping the functional connectivity of anterior cingulate cortex. 
NeuroImage 37, 579–588 (2007). 
59. Calhoun, V. D., Adali, T., Pearlson, G. D. & Pekar, J. J. A method for making group inferences 
from functional MRI data using independent component analysis. Hum. Brain Mapp. 14, 140–
151 (2001). 
60. Wassermann, E. M. Risk and safety of repetitive transcranial magnetic stimulation: report and 
suggested guidelines from the International Workshop on the Safety of Repetitive Transcranial 
Magnetic Stimulation, June 5-7, 1996. Electroencephalogr. Clin. Neurophysiol. 108, 1–16 
(1998). 
61. Yamamoto, D. J., Woo, C.-W., Wager, T. D., Regner, M. F. & Tanabe, J. Influence of 
dorsolateral prefrontal cortex and ventral striatum on risk avoidance in addiction: A mediation 
analysis. Drug Alcohol Depend. (2015). doi:10.1016/j.drugalcdep.2014.12.026 
62. Gallagher, T. A. et al. Characterizing the relationship between functional MRI-derived 
measures and clinical outcomes in patients with vascular lesions. Neurosurg. Focus 34, E8 
(2013). 
63. 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 Stimulat. 7, 226–233 (2014). 
64. Martin, L. et al. A pilot functional MRI study of the effects of prefrontal rTMS on pain 
perception. Pain Med. Malden Mass 14, 999–1009 (2013). 
65. Li, X. et al. Low frequency repetitive transcranial magnetic stimulation of the left dorsolateral 
prefrontal cortex transiently increases cue-induced craving for methamphetamine: a 
preliminary study. Drug Alcohol Depend. 133, 641–646 (2013).