Transcranial Magnetic Stimulation (TMS) in Obsessive Compulsive Disorder (OCD): Mechanisms and Biomarkers
Phase: NA
Conditions: Obsessive Compulsive Disorder
Sponsor: Massachusetts General Hospital

 
 
1 
 
Detailed Protocol: 
 
Title: Transcranial Magnetic Stimulation (TMS) in Obsessive Compulsive Disorder 
(OCD): mechanisms and biomarkers. 
 
Principal Investigator: Joan Camprodon MD MPH PhD 
 
 
Date: August 18th, 2020 
 
I. BACKGROUND AND SIGNIFICANCE:    
 
Obsessive Compulsive Disorder (OCD) 
 
Obsessive-compulsive disorder (OCD) is a severe, chronic disorder, marked by 
recurrent, intrusive and distressing thoughts (obsessions) and/or repetitive behaviors 
(compulsions). OCD among adults in the United States has an estimated lifetime 
prevalence of 2.3% (Ruscio et al., 2010). Available data from systematic reviews and 
meta-analyses support cognitive-behavioral therapy (CBT) as efficacious in reducing 
OCD symptoms (Abramowitz, 1997; Olantunji et al., 2013; Rosa-Alcázar et al., 2008). 
CBT incorporating exposure and response prevention (ERP) is the psychological 
treatment of choice for OCD (NICE, 2006). However, approximately 25% of patients 
who initiate ERP for OCD drop out (Abramowitz et al., 2005). ERP involves gradual 
prolonged exposure to fear-eliciting stimuli or situations while simultaneously refraining 
from compulsive behavior. Cognitive therapy (CT), a form of CBT that focuses on 
modifying dysfunctional beliefs about the presence or significance of intrusive thoughts 
has also proven effective in the treatment of OCD (Wilhelm et al., 2009). A comparison 
of effect sizes between CT and ERP suggest no significant differences between variants 
of CBT for OCD (Abramowitz, 1997; Olantunji et al., 2013; Rosa-Alcázar et al., 2008). 
Findings from meta-analytic studies of CBT for OCD suggest response rates of 50% to 
75% and effect sizes ranging from 0.8 to 1.24 from pre- to post-treatment, but these 
effects may not be durable (Abramowitz et al., 2005; Jónsson & Hougaard, 2009; NICE, 
2006; Olatunji et al., 2013; Ponniah et al., 2013). A recent meta-analysis (Olatunji et al., 
2013) found the overall controlled effect size for CBT was significantly larger at post-
treatment (Hedge’s g = 1.39) compared to follow-up (g = 0.51).   
 
OCD can also be treated with pharmacotherapy, and the preferential efficacy of 
serotonin reuptake inhibitors (SRI’s) led to the so-called serotonin hypothesis (for a 
review of the neurochemical hypothesis of OCD see Goodman, Grice, Lapidus and 
Coffey, 2014).  An important paradigm is moving psychiatry and the clinical 
neurosciences from neurochemical models of disease, which originated from clinical 
observations of pharmacological effects, to a more anatomical and neurophysiological 
circuit-based understanding of emotion, behavior, cognition and their disorders (Haber & 
Rauch, 2009). This paradigm shift is important not only because it provides a new 
understanding of the neuroscientific basis of psychiatric disorders, but also because it 
leads to the development of novel treatment strategies using neuromodulation of selective 

 
 
2 
circuits with device-based interventions (George & Aston-Jones, 2010). TMS is one of 
these treatments, allowing for the directed, noninvasive modulation of neural circuitry 
with relatively few and benign side effects. The need to develop biomarkers that will help 
us understand essential pathophysiological processes in order to develop better clinical 
tools is paramount. Similarly, understanding the mechanism of action of our effective 
interventions must be a first priority strategy to identify target mechanisms that, when 
modified, revert pathological states. 
 
Transcranial Magnetic Stimulation 
Transcranial Magnetic Stimulation (TMS) is a noninvasive neuromodulation 
modality that uses powerful and rapidly changing magnetic fields applied over the 
surface of the skull to generate targeted electrical currents in the brain (Camprodon et al., 
2013). TMS has a well-established safety profile and it is able to modulate brain activity 
without surgery, anesthesia or the generation of a seizure (Rossi et al., 2009). Since its 
development in the mid-1980s, it has become a widely used tool for neuroscience 
research and for clinical applications, both diagnostic and therapeutic. In 2008, the FDA 
approved the use of high frequency repetitive TMS (rTMS) over the left dorsolateral 
prefrontal cortex (DLPFC) for the treatment of MDD (O’Reardon et al., 2007), and in 
2013 the use of deep TMS H-coils for the treatment of MDD was also approved 
(http://www.accessdata.fda.gov/cdrh_docs/pdf12/k122288.pdf). Additionally, in 2018 the 
FDA also approved the use of rTMS for the treatment of OCD. TMS has been shown to 
be a well-tolerated and effective technique that can benefit individuals with MDD 
through the noninvasive modulation of disease-relevant neural circuits. Clinical and 
translational research has also shown good safety and efficacy for other disorders, 
including OCD (Berlim et al., 2013). While different cortical targets have been explored 
for the treatment of OCD, inhibiting the pre-Supplemental Motor Area (pre-SMA) with 
low frequency (1Hz) rTMS has shown the best results so far (Mantovani et al., 2010). 
 
Nevertheless, the literature on TMS for OCD remains noisy and the clinical 
outcomes, while promising, need improvement. An important source for this variability is 
the lack of individualized methods to identify and target the appropriate cortical nodes for 
stimulation. Our study will address this by using individual fMRI maps to identify the 
target and neuronavigation technology to precisely and consistently inhibit it with TMS. 
Another critical variable driving these inconsistencies in the field is the lack of 
mechanistic understanding of the effects of TMS in OCD. A priori hypotheses about 
disease mechanisms have driven the choices of targets of stimulations in the published 
clinical trials (based on a fairly robust understanding of the circuitry involved in the 
pathophysiology of OCD), but most studies limited themselves to measuring clinical 
outcomes. While this pragmatic approach is valuable, it leaves too many open questions 
and requires too many assumptions. Our study will innovate by proposing a translational 
systems neuroscience approach to understanding not only the safety and efficacy of TMS 
in patients with OCD, but also the physiological and anatomical changes driving (or 
resisting) response and remission.  
 
II. Specific Aims 

 
 
3 
 
 
In this proposal we will apply functional neuroimaging-guided inhibitory rTMS to 
the pre-SMA in patients with OCD, integrating neuroimaging, behavioral and clinical 
research tools in a randomized controlled clinical trial. We will assess the safety and 
efficacy of this novel individualized strategy, study the mechanism of action at the 
circuit-level, and identify biomarkers and predictors of response. 
 
Aim 1:  To assess the efficacy of fMRI-guided 1HZ rTMS over the pre-SMA:   
We hypothesize that patients randomized to 6 weeks of daily 1Hz rTMS will have 
a greater reduction in OCD symptom severity than patients randomized to identical 
placebo stimulation.  
 
Aim 2:  To identify the mechanisms of action of TMS at the circuit-level: 
Hypothesis 2.1: We hypothesize that decreased connectivity between the pre-
SMA, the Orbito-Frontal Cortex (OFC) and dorsal Anterior Cingulate Cortex (dACC) 
will correlate with clinical improvement. 
Hypothesis 2.2: We hypothesize that TMS would improve fear extinction 
retention from pre- to post-treatment, and increase functional activation of the fear 
extinction network during extinction recall. The extinction network includes the 
ventromedial prefrontal cortex (vmPFC), dACC, and hippocampus. 
 
Aim 3:  To identify biomarkers and predictors of treatment response: 
Hypothesis 3.1: We hypothesize that pathological hyperconnectivity between the 
pre-SMA, the Orbito-Frontal Cortex (OFC) and dorsal Anterior Cingulate Cortex 
(dACC) will correlate with OCD symptom severity in the pre-TMS scans. 
Hypothesis 3.2: We hypothesize that (a) pathological hyperconnectivity between 
the pre-SMA the Orbito-Frontal Cortex (OFC) and dorsal Anterior Cingulate Cortex 
(dACC) and (b) early reduction of this pattern after week 1 of TMS will predict positive 
clinical outcomes after TMS. 
 
III.  SUBJECT SELECTION 
 
 
There will be two different groups of patients with OCD in this study: one will 
receive active stimulation and one sham stimulation (i.e. placebo).  We will recruit as 
many patients as are needed in order to obtain 20 completers per group (i.e. 40 patients in 
total). We expect an early termination rate of 30%, and we will anticipate the need to 
recruit and consent a total of 58 patients overall.  
 
Inclusion Criteria: 
1) 18-65 years of age. 
2) Proficient in English. 
3) A diagnosis of primary OCD (as determined by SCID). 
4) Yale-Brown Obsessive Compulsive Scale total score ≥ 16. 
5) Normal (or corrected) vision. 
6) Stable OCD medication regimen or OCD medication free for ≥ 12 weeks prior to 
study. 

 
 
4 
7) Able to give informed consent. 
 
Exclusion Criteria: 
1) Current or history of neurologic or psychiatric disease (e.g., mental retardation, 
dementia, brain damage, or other cognitive impairment) that would interfere with 
ability to engage in TMS 
2) Psychopathology not appropriate for the treatment (e.g., manic episode or 
psychosis)  
3) Substance abuse or dependence that is current or within the last six months or use 
of an illicit drug that is not prescribed, as indicated by a urine drug screen and/or 
clinical inference.  
4) Use of anticonvulsants within 2 weeks prior to study (to be ruled out by a urine 
drug screen. 
5) Use of Tricyclic Antidepressants (e.g. Clomipramine). 
6) Use of psychotropic medication is allowed (except for the limitations specified on 
use of OCD regimens, anticonvulsants, and tricyclic antidepressants). 
7) Documented resistance to 4 or more valid pharmacological trials of 2 or more 
different medication classes (e.g. SSRIs and TCAs).  
8) Previous exposure to TMS. 
9) Major/chronic medical conditions. 
10) History of head injury resulting in prolonged loss of consciousness and/or 
neurological sequelae. 
11) Prior neurosurgical procedure. 
12) Metal in the body that is ferromagnetic, metal injury to the eyes 
13) History of seizures. 
14) Implanted pacemaker, medication pump, vagal stimulator, deep brain stimulator, 
TENS unit, or ventriculo-peritoneal shunt 
15) Pregnancy; breastfeeding or nursing; for women of childbearing a pregnancy test 
(to be ruled out by urine β-HCG) will be conducted prior to study. 
16) Currently in Cognitive Behavioral Therapy (CBT). 
17) Diagnosis of primary sleep disorder such as primary insomnia, narcolepsy, sleep 
apnea, shift work sleep disorder and others.  Sleep disorders such as insomnia or 
hypersomnia that are secondary to depression or OCD are permitted. 
18) Current clinically significant suicidality  
 
Recruitment: 
 
OCD patients will be recruited from the Obsessive-Compulsive Disorder Clinic at 
MGH, as well as elsewhere in the hospital or in its clinics. Persons inquiring to other 
hospital research and clinical staff about OCD research opportunities may also be 
included. Subjects may be recruited from persons who have listed themselves on the 
RSVP for Health Recruitment Registry or ResearchMatch.org as volunteers to participate 
in OCD research. We may also use paid advertising on the internet, television, and 
Boston MBTA in order to reach out to more potential subjects. Efforts will be made to 
attain a mix of study participants, in terms of gender and racial/ethnic representation, that 
is reflective of the respective populations under study. Specifically, with regard to 
gender, it is anticipated that half of the subjects will be male, and half will be female.  
Similarly, it is anticipated that minority representation in the study cohorts will be 

 
 
5 
reflective of the respective populations under study. In the event that early recruitment 
efforts yield under-representation of any of the above groups, active outreach will be 
initiated (through advertisement) in an effort to achieve the above goals.   
 
IV.  SUBJECT ENROLLMENT 
 
 
The formal recruitment will take place at the first study visit to MGH. Necessary 
data will be gathered for the subject identification and remuneration, including date of 
birth, gender, years of education, address, telephone number, and social security number.  
Informed consent will be obtained by doctoral level licensed clinician study-staff or study 
staff that are doctoral candidates under the supervision of doctoral-level licensed 
clinicians prior to participation in the study using the e-consent module in REDCapTM 
and IRB-approved consent form. If the participant agrees to participate, the consent form 
will be signed electronically by the research participant and the individual obtaining 
consent. Each participant will be emailed a copy of the signed and dated consent form. 
 
V.  STUDY PROCEDURES 
 
 
This study will have 2 phases: phase 1 will be a randomized, double-blinded, 
placebo-control trial, and phase 2 will be an open-label trial in which all patients who did 
not remit after completion of phase I (regardless of treatment group), will be invited to 
receive an additional 6 weeks of real TMS. Remitters will be defined as those subjects 
who have a week six Y-BOCS score of 12 or less. 
 
1. Subjects Assessment 
All candidates for this project will undergo a comprehensive clinical evaluation including 
a clinical interview to establish relevant psychiatric, medical and neurological history and 
a structured clinical interview (Structured Clinical Interview for DSM-IV Axis I 
Disorders; SCID-I) to confirm psychiatric diagnoses, the Edinburgh Handedness 
Inventory – Short Form (EHI) to establish handedness, the Beck Depression Inventory 
(BDI), HAM-D, and PHQ-9 to quantify depressive symptoms, the Beck Anxiety 
Inventory (BAI) to quantify general anxiety symptoms, the Speilberger State Trait 
Anxiety Inventory (STAI) to quantify trait vs. state aspects of anxiety, the Anxiety 
Sensitivity Index (ASI) to quantify anxiety sensitivity, and a covid-19 impact scale to 
assess the effect of the coronavirus pandemic in the past week. The Yale Brown 
Obsessive-Compulsive Scale (YBOCS), Obsessive-Compulsive Beliefs Questionnaire-44 
(OBQ), the Compulsive Behaviors Form (CBF), and Obsessive-Compulsive Inventory 
(OCI-R) will be used to measure OCD symptoms and severity. The Clinical Global 
Impressions (CGI) rating scale will be used as an additional measurement of clinical 
change from baseline (see detailed description below). In addition, the Rey-Osterrieth 
Complex Figure Test and the Stroop and Wisconsin Card Sorting tasks will be conducted 
to assess relevant aspects of executive function and selective attention (see detailed 
description below). The initial evaluation of all subjects will be audio recorded for inter-
rater reliability, and therapist supervision purposes. The digital recordings will contain 
participant ID numbers but not names or other identifying information. Digital recordings 
will be rated and simultaneously entered in the REDCap data management system for 

 
 
6 
analysis. The digital recordings will be kept by the research team in a password-protected 
file and destroyed after completion of the study. When possible, assessments will be used 
from previous studies in the OCD program (to which participants will consent). 
 
After the initial thorough baseline assessment, patients will be assessed longitudinally 
through the course of the study at weeks 2, 4, 6 and 3 months after the last TMS session. 
These assessments will be shorter and will include the following measures: YBOCS, 
OBQ, OCI-R, BDI, HAM-D, PHQ-9, and covid-19 impact scale. The Rey-Osterrieth 
Complex Figure Test and the Wisconsin Card Sorting and Stroop tasks will also be 
conducted at week 6. 
 
As of July 8th, 2020, a majority of the clinical assessment procedures will take place 
virtually using Healthcare Secure ZoomTM, including the SCID-I, Y-BOCS, EHI, HAM-
D, CGI, and CBF. Likewise, the following self-report instruments will be administered 
remotely via REDCapTM: the demographics form, OCIR, ASI, OBQ, PHQ-9, CGI – 
Patient Version, and covid-19 impact scale. Additionally, subjects will be screened for 
covid-19 symptoms before attending in-person clinical study visits (baseline, week 6, and 
3-month follow-up for phases 1 and 2) by completing a covid-19 symptom attestation 
form via REDCapTM. Administration of the ROCF, WCST, Stroop, Goal-Oriented versus 
Habit-Driven Behaviors Task, BDI, BAI, STAI, and urine drug screen, however, will 
take place in-person at the MGH-main campus (Simches). 
 
 
Clinical Global Impressions (CGI) scale 
 
This global rating scale, which ranges from 1 (very much improved) to 7 (very 
much worse) (Rush et al, 2008), is commonly used in pharmacotherapy trials. Subjects 
and will complete a CGI for OCD symptoms (CGI-OCD) and for overall symptoms 
(CGI-global) at each assessment, captured in the CGI-Patient form. The independent 
evaluator will complete the CGI-OCD and CGI-global at each assessment, captured in 
the CGI-Clinician form. Additionally, the independent evaluator will complete a CGI-
Severity (CGI-S) at baseline. 
 
Rey-Osterrieth Complex Figure Test  
The Rey-Osterrieth Complex Figure Test (ROCF; Rey, 1941 and Osterrieth, 
1944) evaluates visuospatial ability, executive functioning, and memory. Subjects 
reproduce a complicated line drawing by copy and then by memory. Participants will be 
asked to copy the figure with the stimulus card present (copy trial), and later to reproduce 
the figure from memory at two separate time points (3 min delay and 30 min delay) 
followed by a recognition portion. We will score organization (executive function) and 
accuracy (memory) using the Savage (Savage et al., 1999) and Denman (Denman, 1984) 
systems, respectively. The ROCF is sensitive to change (Buhlmann et al., 2006; Park et 
al., 2006; Kang et al., 2003; Kuelz et al., 2006). 
  
The Wisconsin Card Sorting Task 
The Wisconsin Card Sorting Test (WCST; Grant & Berg, 1981) is a measure of 
executive function and strategic planning, particularly with the ability of an individual to 
use feedback from the environment to shift individual cognitive sets and experience goal-

 
 
7 
directed behavior. During this 10-15 minutes test, four stimulus cards incorporating color, 
form, and number are presented to a subject. The subject must sort the cards according to 
different rules or principles and be able to change their approach throughout the test 
administration. 
 
The Stroop Task 
The Stroop Task (Stroop, 1935) employs both controlled (color naming) and 
automatic (reading) processes. Participants are asked to complete the approximately five-
minute task by naming the color ink of a word while ignoring the meaning of the word. In 
the classic Stroop Task, all words are the names of colors. The difference in time between 
naming the ink of a different color-word  (e.g. the word ‘red’ is written in blue ink) 
versus naming the ink of a same color-word  (e.g. the word ‘red’ is written in red ink) 
demonstrates the interference of automatic reading processes on controlled color naming 
processes.   A larger Stroop interference represents difficulty with selective attention 
processes, processing speed, and response inhibition (e.g.  Ben-David, Nguyen, van 
Lieshout, 2011).  
 
Goal-Oriented versus Habit-Driven Behaviors Task 
This 20-minute computer-administered task measures whether behaviors are 
planned and purposeful (e.g., goal-oriented) or automatic (e.g., habit-driven). In the two-
step task, participants first learn to associate certain behaviors (i.e., choosing one image 
of a fractal over another) with rewards. Then, the rewards are devalued, and we assess 
whether the participants’ choices change in response to the devaluation. This task has 
been piloted and tested with healthy individuals (Gillan et al., 2015), as well as 
individuals with OCD (Gillan et al., in preparation). In order to incentivize participants 
during the task, a small monetary award is promised based on participation 
(approximately $1, no more than $2 per administration). 
 
2. Patient randomization and clinical trial design 
2.1 Phase 1: Randomized placebo controlled 
Patients will be randomized to receive real or sham rTMS using the variable-sized 
permuted blocks randomization algorithm (with a maximum block size of 4), with 
randomization stratified by symptom severity (Y-BOCS total score ≤31 vs. >31) and age 
(≤ 34 vs. >34 years at entry), to ensure balance in these factors across arms in this small 
study.  Randomizations will be conducted in SAS using procedures outlined by Efird 
(2011) [Efird, J. Blocked Randomization with Randomly Selected Block Sizes. Int. J. 
Environ. Res. Public Health., 8, 15-20].  The randomization codes generated by our 
statistician/data manager for each of the 4 strata will be communicated to the study 
coordinator at the time of randomization (i.e. at the patient's baseline visit), based on the 
stratum to which the patient belongs. Patients will be blinded to their treatment arm. 
Patients will receive 6 weeks of daily treatments Monday through Friday (not on 
weekends). There will be 3 MRI sessions that will take place (i) within 2 weeks prior to 
the first TMS session, (ii) at the end of week 1 of treatment and (iii) within 2 weeks after 
the last TMS session. Longitudinal clinical assessments will take place throughout the 
course of study at the end weeks 2, 4 and 6 of TMS, in addition to a last assessment 3 
months after TMS. Physiological assessments to measure cortical excitability and 

 
 
8 
plasticity in the motor cortex using single and paired-pulse TMS will also take place the 
1st day of treatment and at the end of weeks 2, 4 and 6 of TMS. 
 
Figure 1: Clinical Trial Design for Phase 1
 
 
2.2 Phase 2: Open label TMS 
Patients who do not remit following the double-blinded phase of the study will be 
given the option to enter an open-label phase 2 and receive an additional 6 weeks of real 
rTMS (regardless of whether they received real or sham TMS in phase 1). Phase 2, like 
phase 1, will also include an MRI session at the end of week 1 and at the end of week 6, 
clinical assessment at weeks 2, 4 and 6, and TMS neurophysiological assessments at the 
end of weeks 2, 4 and 6. 
 
3. Transcranial Magnetic Stimulation 
We will use a MagVenture MagPro X100 with MagOption stimulator and two dynamic 
cooled butterfly coils: one real and one sham (MagVenture, Denmark) navigated with an 
infrared TMS Neuronavigation Research Premium system (Localite, Germany) to 
administer the TMS. 
 
We will start by measuring the patient´s motor threshold (MT), which is a measure of 
cortical excitability used to standardize the intensity of stimulation across subjects. To do 
this, the TMS coil is placed over the hand representation of the primary motor cortex 
(M1) in a rostro-medial 45º angle. Single pulses are applied with an interpulse interval 
(IPI) of at least 7 seconds, to prevent additive neuromodulatory effects. When pulses are 
applied at suprathreshold intensities, a volley of activity travels through the pyramidal 
motor pathways and leads to the contraction of the contralateral hand muscle. The 
intensity of stimulation is sequentially reduced until we reach a point when fewer than 
50% of the pulses (usually <3 out of 6) lead to a muscle contraction (identified by visual 
inspection or neurophysiological motor evoked potentials). The first TMS intensity that is 
unable to elicit a muscle contraction more than 3 out of 6 pulses is considered the motor 
threshold, and usually expressed as a percentage of the maximum stimulator output. 
 
Once the MT is determined, we will define our target of stimulation using the fMRI 
data obtained and analyzed days prior to the first TMS session. Using the Localite TMS 
neuronavigation system we will corregister the patient´s head to their MRI, and place the 
TMS coil over the scalp position that corresponds to the cortical target. 
 

 
 
9 
At this point, the treatment session can begin. We will use an inhibitory protocol of 
1Hz stimulation at 110% of the motor threshold intensity and a total of 1800 pulses per 
session. This will have a duration of 30 min. This protocol complies with published 
safety guidelines (Rossi et al., 2009). Sessions will occur daily Monday through Friday 
over 6 weeks. 
 
Sham TMS will use the exact same procedure with a sham coil, which is designed to 
induce the same nonspecific sensory effects of TMS (auditory and somatosensory 
activation) without inducing the neuromodulatory magnetic fields.  
 
4. Transcranial Magnetic Stimulation neurophysiological measures 
The history of TMS has been closely linked to motor neurophysiology; the first 
applications were indeed geared towards the study of cortical physiology in humans. 
Both single and paired-pulse protocols have been established to study cortical excitability 
and plasticity (both intracortical inhibition and facilitation). These protocols lead to 
robust effects in healthy controls and are used regularly in clinical practice for diagnostic 
applications in neurological populations (Groppa et al., 2012). Pharmacological studies 
have been conducted to understand the neurochemical dynamics driving these effects 
(Paulus et al., 2008). Like all other TMS applications, these measures are noninvasive, 
and their safety has been well established (Rossi et al., 2009). 
OCD results from the deficits in cortical inhibition and excessive excitatory states 
within specific brain networks. TMS measures of excitability, inhibition and facilitation 
have been shown to be abnormal in patients with OCD, to reflect clinical severity and to 
respond to treatment (Mantovani et al., 2013). We will use the following measures to 
obtain neurophysiological markers of severity and response, in addition to the other 
neuroimaging and behavioral measures described in this protocol.  
 
The following 5 measures will be assessed: 
• Resting Motor Threshold 
• Active Motor Threshold 
• Cortical Silent Period 
• Short-Interval Cortical Inhibition (SICI). 
• Intracortical Facilitation (ICF) 
 
4.1 Motor threshold (resting and active) 
The resting motor threshold (RMT)/active motor threshold (AMT) are defined as the 
minimum TMS intensity needed to induce a muscle contraction or motor evoked 
potential larger than 50 mV in at least 50% of the trials, typically 3 of 6 or 5 of 10 TMS 
pulses (Groppa et al., 2012). We will reassess the motor threshold following the same 
procedure used on day 1 of treatment and described above. The resting motor threshold is 
measured with the muscle of study at rest. The active motor threshold is measured with a 
voluntary contraction of the muscle. Motor thresholds are measures of cortical 
excitability and reflect the activity of neuronal membranes and the excitability of 
corticocortical axons, synapses and their sodium channels (NMDA-associated 
transmission and GABAergic mechanisms are less involved) (Ziemann, 2004). 
 
4.2 Cortical silent period 
The cortical silent period CSP is defined as a reduction of the ongoing tonic muscle 
activity, lasting up to 300 ms after the contralateral TMS pulse to the primary motor 
cortex. It can be subdivided into the contralateral (CSP) and the ipsilateral (ISP) cortical 
silent period. Its length can be defined as the duration from the beginning of an MEP to 

 
 
10 
the re-emergence of baseline EMG-activity. The first 50 ms represent spinal mechanisms 
of inhibition, while later inhibition is influenced by cortical networks (Chen et al., 1999). 
This inhibitory phenomenon is thought to be driven primarily by GABAB-receptors 
(Ziemann, 2004). The ISP represents neuronal activity on the muscle ipsilateral to the site 
of stimulation and, therefore, measures cortical phenomena contralateral to the site of 
stimulation and the function of the corpus callosum connecting both hemispheres (Meyer 
et al., 1995). 
  
4.3 Short-interval intracortical inhibition (SICI)  
The application of a first stimulus below motor threshold followed by a second 
stimulus above motor threshold, with a short interstimulus interval (ISI) of 1-5 ms, results 
in a physiological reduction of cortical excitability (short-interval intracortical inhibition 
(SICI)) (Groppa et al., 2012). This parameter reflects inhibitory effects mediated mainly 
by GABAA-receptors, as well as dopamine and acetylcholine (Ziemann, 2004).  
 
4.4 Intracortical facilitation (ICF) 
The same paired-pulse configuration used in SICI but applied at longer ISIs of 10-17 
ms or longer results in increased excitability (Groppa et al., 2012). This intracortical 
facilitation (ICF) seems to be mediated principally by glutamatergic neurotransmission, 
although the underlying physiology is less clear in this case (Ziemann, 2004). 
 
5. Magnetic Resonance Imaging 
 
MRI sessions will include both structural and functional imaging. MRI scanning will be 
conducted on a 3T scanner equipped with multichannel receivers. Protocols have been 
developed and validated at the Martinos Center for Biomedical Imaging. Resting state 
MRI sessions will be obtained while participants focus on a fixation cross (functional 
scans) or have their eyes closed (structural scans). Task fMRI scans (see fear 
conditioning below) will be performed while subjects have their eyes open and receive 
visual, auditory and somatosensory stimulation via integrated MRI-compatible 
audiovisual equipment. Patient will be required to respond using an MRI-compatible 
fiber-optic response-box.  
 
5.1 Multi-Source Interference Task 
The Multi-Source Interference Task (MSIT) is a cognitive paradigm that was 
designed to reliably identify the cingulo-frontal-parietal cognitive/attention network (CFP 
network) within individual subjects (Bush et al., 2006). Among other relevant brain 
regions, the pre-SMA and dorsal anterior cingulate cortex are activated in this 10-15 task 
when performed inside of the scanner. We will use this fMRI cognitive paradigm to 
identify our target of stimulation on an individual-subject basis. 
Briefly, the MSIT presents a string of 3 numbers to subjects, which can be different 
combinations of 0,1,2,3 in which 2 numbers are the same and one is different (e.g. 100, 
323, 221). Subjects are asked to identify what number is different by pressing one of 3 
buttons available in the response box. Some trials will be congruent (e.g. 100) and some 
incongruent (010), depending on the position of the number that is different. Analyzing 
the brain response to these congruent and incongruent trials has been shown to robustly 

 
 
11 
identify these brain networks and regions at the individual level in healthy and patient 
populations (Bush et al., 2006). 
 
5.2 Fear Conditioning:   
All subjects randomized prior to February 27th, 2018 will undergo a 2-day fear 
conditioning protocol. The experiments will be carried out in a 3T scanner.  In 
accordance with previously published procedures (Orr et al., 2000), prior to the initiation 
of the experiment, recording electrodes will be attached to the palm of the subject’s non-
dominant hand to measure SCR, and stimulating electrodes will be connected to two 
fingers of the subject’s dominant hand through which the electric shock will be delivered.  
SCR will be measured through 9-mm (sensor diameter) Sensor Medics Ag/AgCl 
electrodes (safe for use in the magnet environment).   
 
Subjects will view two rooms presented on a monitor, both of which differ in color 
and content (e.g., an office and a kitchen).  The conditioned stimuli (CSs) will be 
different cues in the room. The CS+ and the US will only be presented in one context 
(conditioning context; CX+).  The other context will be used for extinction training (CX-
).  Neither CS+ presented in the context of the CX-, nor a CS- presented in either context, 
will ever be paired with the US. In one of the four possible CS-CX combinations, for 
example, the office will serve as the dangerous context, in which the subjects will receive 
electric currents only when the yellow light is on, whereas the kitchen will serve as the 
safe context, in which no electric currents will be delivered. On day 2, subjects are placed 
back in the magnet and will be presented with additional extinction trials to CSs in 
pseudorandomorder.  
 
As of February 27th, 2018, the fear conditioning paradigm will no longer be 
administered to participants in this study. All fear conditioning data collected prior to 
February 27th will be analyzed. 
 
 
VI.  BIOSTATISTICAL ANALYSIS 
 
Analysis of Aim 1:  To compare change in OCD symptom severity by treatment 
group, we will utilize a mixed effects linear regression model with Y-BOCS total score as 
the outcome, a random effect for time (study week), a fixed effect for group (rTMS 
versus placebo stimulation), and the interaction between time and treatment group (which 
is the covariate of interest).  We will also control for potential confounding by any 
covariates that are significantly imbalanced between the treatment groups. 
Analysis of Aim 2.1:  Specification of the analyses examining whether decreased 
connectivity between the pre-SMA, the Orbito-Frontal Cortex (OFC) and dorsal Anterior 
Cingulate Cortex (dACC) correlates with clinical improvement can be found below, in 
the MRI data section - Resting state Connectivity Analysis.  
Analysis of Aim 2.2:  Specification of the analyses examining whether TMS 
improves fear extinction retention from pre- to post-treatment, and whether increased 
functional activation of the fear extinction network (vmPFC, dACC, and hippocampus) 
during extinction recall can be found below, in the MRI data section - Fear Extinction.  

 
 
12 
Analysis of Aim 3:  To identify biomarkers and predictors of treatment response, we 
will us linear regression analysis to examine predictors of clinical improvement.  The 
outcome will be the post-treatment Y-BOCS score, the covariate of interest will be each 
ROI (examined in separate models to begin with, to avoid issues of collinearity), and we 
will control for the pre-treatment Y-BOCS score. Univariately significant predictors will 
then be entered into a multivariate linear regression model using step-wise selection 
(alpha=0.10 to enter, alpha=0.05 to stay).   
 
TMS neurophysiology 
Numeric values for each of our 5 physiological measures will be obtained at each 
of the established times (baseline, week 2, week 4, week 6). A general linear model will 
be used to assess the difference in longitudinal progression of these measures between the 
active and placebo group. 
 
MRI data 
 
Fear Extinction 
During the extinction training on day 1, the principal contrast for the fMRI data 
will be CS+ vs. CS-, to establish the substrates of extinction learning.  During extinction 
recall on day 2, the principal contrast will be CS+1 vs. CS+2.  Analyses of SCR will test 
for group x condition interactions for OCD vs. HC with respect to CS2 vs. CS1.  With 
respect to the fMRI data, the initial analysis will involve assessing for a main effect of 
condition (across both groups), by comparing CS1 vs. CS2 during extinction learning and 
recall.  Finally, voxel-wise analyses will also be performed to identify specific loci of 
significant group x condition interactions (OCD, HC x CS2, CS1) within designated 
prefrontal search territories of interest.   
With respect to SCR analyses, the SCR for each CS will be calculated by 
subtracting the mean level for the 2 s immediately preceding CS onset from the highest 
value among those recorded during the 10-s CS interval. Values will be subjected to 
repeated measures ANOVA to test hypotheses regarding both within-group and between-
group differences at an alpha of p<0.05.  Significant interactions will be followed-up with 
t-tests where indicated. Change in clinical symptoms from pre- to post- treatment, such as 
the change in YBOCS scores will be correlated with improved extinction retention index 
from pre- to post-treatment. Voxel-wise regression using change in YBOCS scores will 
be conducted against BOLD responses during extinction learning and recall to identify 
changes in functional activation of brain regions associated with change in clinical 
symptoms. 
 
Multi-Source Interference Task 
Standard task-based neuroimaging analyses procedures will be used to analyze the 
brain activity resulting from patients performing the MSIT in the scanner, including pre-
processing and statistical parametric mapping using general linear models. 
 
Resting state Connectivity Analysis 
Functional connectivity analysis will employ a regional approach based on Biswal 
(Biswal, Yetkin, Haughton, & Hyde, 1995) and extended in Fox (Fox et al., 2005) and 
Vincent (Vincent et al., 2006). This approach has been previously used by our group and 

 
 
13 
others to study the medial temporal lobe memory system (Kahn, Andrews-Hanna, 
Vincent, Snyder, & Buckner, 2008), the frontal parietal control system involved in 
executive function (Vincent et al., 2006), and the default network (Buckner & Vincent, 
2007), as well as initial explorations in aging (Andrews-Hanna et al., 2007). Following 
standard preprocessing, several additional steps are employed to condition the fMRI data 
for analysis of voxel-based correlations (described in Vincent 2006). Temporal band-pass 
filtering will remove frequencies greater than 0.08Hz.  Several sources of spurious 
variance along with their temporal derivatives will then removed from the data through 
linear regression: (i) six parameters obtained by rigid body correction of head motion, (ii) 
the whole-brain signal averaged over a fixed region in atlas space, and (iii) the signal 
from a ventricular region of interest and a region centered in the white matter. In this 
manner, variance unlikely to be involved in spatially specific regional correlations is 
removed from the data. The global (whole brain) signal may correlate with respiration-
induced fMRI signal fluctuations or result from global fluctuations in neuronal activity. 
Removing signals correlated with ventricles and white matter is an additional means of 
reducing non-neuronal contributions to BOLD correlations (Fox et al., 2005). Following 
preparation, maps of functional connectivity are obtained by plotting the correlation 
strength at each voxel to the time course of a seed region. Between-region correlations 
are obtained by extracting the time courses for multiple regions and computing the 
correlation coefficient between regions pairs. ANCOVA analysis will be performed 
covarying for the clinical and behavioral measures.  
 
Cortical reconstruction and volumetric analysis 
Cortical reconstruction and volumetric segmentation will be performed with the 
Freesurfer image analysis suite, which is documented and freely available for download 
online (http://surfer.nmr.mgh.harvard.edu/). The technical details of these procedures are 
described in prior publications (Dale et al., 1999; Dale and Sereno, 1993; Fischl and 
Dale, 2000; Fischl et al., 2001; Fischl et al., 2002; Fischl et al., 2004a; Fischl et al., 
1999a; Fischl et al., 1999b; Fischl et al., 2004b; Han et al., 2006; Jovicich et al., 2006; 
Segonne et al., 2004)). Briefly, this processing includes motion correction and averaging 
(Reuter et al. 2010) of multiple volumetric T1 weighted images (when more than one is 
available), removal of non-brain tissue using a hybrid watershed/surface deformation 
procedure (Segonne et al., 2004), automated Talairach transformation, segmentation of 
the subcortical white matter and deep gray matter volumetric structures (including 
hippocampus, amygdala, caudate, putamen, ventricles)(Fischl et al., 2002; Fischl et al., 
2004a) intensity normalization (Sled et al., 1998), tessellation of the gray matter white 
matter boundary, automated topology correction (Fischl et al., 2001; Segonne et al., 
2007), and surface deformation following intensity gradients to optimally place the 
gray/white and gray/cerebrospinal fluid borders at the location where the greatest shift in 
intensity defines the transition to the other tissue class (Dale et al., 1999; Dale and 
Sereno, 1993; Fischl and Dale, 2000). Once the cortical models are complete, a number 
of deformable procedures can be performed for further data processing and analysis 
including surface inflation (Fischl et al., 1999a), registration to a spherical atlas which 
utilized individual cortical folding patterns to match cortical geometry across subjects 
(Fischl et al., 1999b), parcellation of the cerebral cortex into units based on gyral and 
sulcal structure (Desikan et al., 2006; Fischl et al., 2004b), and creation of a variety of 

 
 
14 
surface based data including maps of curvature and sulcal depth. This method uses both 
intensity and continuity information from the entire three dimensional MR volume in 
segmentation and deformation procedures to produce representations of cortical 
thickness, calculated as the closest distance from the gray/white boundary to the 
gray/CSF boundary at each vertex on the tessellated surface (Fischl and Dale, 2000). The 
maps are created using spatial intensity gradients across tissue classes and are therefore 
not simply reliant on absolute signal intensity. The maps produced are not restricted to 
the voxel resolution of the original data thus are capable of detecting submillimeter 
differences between groups. Procedures for the measurement of cortical thickness have 
been validated against histological analysis (Rosas et al., 2002) and manual 
measurements (Kuperberg et al., 2003; Salat et al., 2004). Freesurfer morphometric 
procedures have been demonstrated to show good test-retest reliability across scanner 
manufacturers and across field strengths (Han et al., 2006; Reuter et al., 2012). 
To extract reliable volume and thickness estimates, images will be automatically 
processed with the longitudinal stream in FreeSurfer (Reuter et al., 2012). Specifically an 
unbiased within-subject template space and image (Reuter and Fischl, 2011) is created 
using robust, inverse consistent registration (Reuter et al., 2010). Several processing 
steps, such as skull stripping, Talairach transforms, atlas registration as well as spherical 
surface maps and parcellations are then initialized with common information from the 
within-subject template, significantly increasing reliability and statistical power (Reuter 
et al., 2012). 
 
Diffusion MRI and Tractography 
Diffusion images will be corrected for motion and eddy currents, using affine 
registration to a b0 reference volume, by using FSL-based software. A tensor model will 
then be fitted to each voxel, generating different diffusivity maps. Finally, we will use 
TBSS (Smith et al., 2006) to perform voxel-wise statistical analyses for relevant 
diffusivity measures: functional anisotropy (FA), and mean axial and radial diffusivity 
(MD, AD, RD). 
Two-tensor whole brain tractography will be implemented for tract analysis. This 
form of tractography involves a recursive process that both fits local parameters at each 
step and propagates the fiber in the most stable direction, allowing simultaneous 
tractography and model estimation. This model also uses a covariance matrix to measure 
confidence, which reduces false positives, and implements two eigenvalues per voxel, 
which improves resolution of and accounts for branching and crossing fibers. Whole-
brain tractography will be used to best account for all potential tracts among the ROIs, 
which may be otherwise limited in solely ROI-based tractography (Yang et al., 2014). 
 
Power for the Primary Aim 
For the primary analysis comparing change in Y-BOCS total score by treatment group, 
with 20 patients per group and a two-sided alpha of 0.05, we will have 90% power to 
detect an effect size of 1.06.  Due to the novelty of our approach, no comparable data 
exist to inform the power calculation.  However, Mantovani et al. (2010) examined 
change in Y-BOCS total score among 18 OCD patients (completers) randomized to 4 
weeks of similar rTMS or sham treatment.  Based on their reported F-statistic comparing 
change in Y-BOCS total score after 4 weeks of treatment, their observed effect size, 

 
 
15 
expressed as Cohen's d (Thalheimer & Cook, 2002), was 1.8.  Based on these results, we 
believe that our study will be adequately powered to detect anticipated effect sizes. 
 
VII.  RISKS AND DISCOMFORTS 
 
Participation in this study poses minimal foreseeable risks. Patients may experience 
psychological discomfort when discussing psychiatric history and/or current psychiatric 
symptoms or side effects.  Answering detailed questionnaires may also create some 
inconvenience for subjects. Confidentiality of all subjects will be protected per 
institutional and federal requirements.  
 
Repetitive TMS at high frequencies has the rare potential to induce a seizure even in 
healthy individuals (~ 8 reported cases worldwide since the invention of TMS in 1985), 
but the risk for seizure can be effectively managed by using appropriate subject selection 
criteria and by keeping the TMS parameters within the safety parameters of the 
international safety consensus (Rossi et al., 2009). In addition, this study proposes the use 
of low-frequency rTMS, which has a cortical inhibitory effect, and single-pulse TMS for 
physiological assessments, which does not have carry-over cumulative effects. To protect 
subjects from the risk of a seizure, this study strictly adheres to the international safety 
consensus and will exclude subjects at increased risk for seizures (Rossi et al., 2009). In 
the unlikely event that a suspected seizure would occur, the seizure stops as soon as TMS 
is stopped, and the TMS clinical service safety plan is activated, including transfer of the 
subject with ambulance to MGH ER for a medical and neurological evaluation.  No one 
has ever developed epilepsy as a consequence of TMS. There are no known or foreseeable 
long-term risks associated with TMS. TMS is not uncomfortable but the subject may feel 
twitching of the scalp muscles during stimulation. It is possible that the subject may feel 
headache after the TMS measurement that is caused by keeping the head in the same 
position for a lengthy period of time and/or by stimulation of the scalp muscles and nerves 
by the TMS pulses. The headache, if present, is typically mild, disappears soon and, if 
needed, can be treated by mild over-the-counter analgesics that are available to study 
participants.  
 
The risks of the MRI examination are essentially identical to those of a standard, 
clinically employed, anatomical MRI study.  There is no ionizing radiation.  The 
technique is non-invasive and we will not administer any contrast agent.  A certain 
percentage of people cannot tolerate the MRI environment, usually due to claustrophobia. 
We will monitor subjects and we will be in constant auditory contact with the subjects.  
The study can be immediately stopped at the subject’s request.  The MR machine will 
make a number of sounds; the intensity of these sounds is not harmful to one’s hearing if 
one wears a headphone or earplugs which will be routinely provided to our subjects.  
Contraindications to participating in MRI studies exist for those subjects who have 
metallic or other electronic devices implanted such as pacemakers, electronic implants, 
shrapnel in the eye, certain intracranial aneurysm clips, etc.  All subjects will fill out a 
detailed questionnaire with regard to any implanted metal or devices or any shrapnel 
injuries.  These contraindications constitute reasons for exclusion from the present study. 

 
 
16 
The safety of our fear conditioning paradigm has been established and approved in 
multiple IRB-approved research protocols world-wide and at MGH. The electrical shock 
that will be applied to the fingers during the fear conditioning paradigm is uncomfortable 
but not painful.  The finger stimulator is powered by a 9V battery and has been approved 
by Partners Biomedical Engineers. 
 
 
1. 
PROTECTION AGAINST RISK  
 
 
 
All potential subjects will be screened for TMS and MRI risk factors prior to 
study enrollment. If the potential subject cannot rule out the possibility of pregnancy, a 
pregnancy test will be conducted prior to study enrollment. All enrolled subjects will 
again be screened just before undergoing MRI. These screening procedures should 
exclude subjects with foreseeable risks. All subjects will be monitored continuously by 
research investigators during MRI sessions. Subjects will be able to communicate with 
research investigators throughout all experimental sessions, including during MRI scans 
via a 2-way microphone. All subjects will wear earplugs during MRI and TMS.  Earplugs 
will reduce the transmission of noises of the TMS coils and MRI scanner (e.g., buzzing, 
beeping) to a comfortable and safe level. If a subject experiences any discomfort that 
cannot be alleviated by the research investigators, the experimental session will be 
terminated. 
 
VIII.  POTENTIAL BENEFITS 
 
 
Patient may benefit from this intervention by improving their symptoms of OCD. 
Patient randomized to the placebo arm will be given the option to enter an open-label 
phase when they will receive an additional 6-weeks of active rTMS.  
 
IX.  MONITORING AND QUALITY ASSURANCE 
 
 
All study procedures will be in accordance with the MGH subcommittee on 
Human Studies.  The principal investigator will oversee the collection, maintenance, and 
analysis of the data.  Research Affairs will be contacted immediately in the case of 
unexpected adverse events.   
 
A Data and Safety Monitoring Board will review the progress of the study annually, 
discuss any safety concerns that arise, and make recommendations to improve safety 
procedures if indicated. No member of the DSMB is otherwise involved with the study. 
Board members will consist of Mark Vangel, Ph.D., Assistant Professor of Radiology, 
Harvard Medical School; Scott Rauch, MD, President and Psychiatrist in Chief, McLean 
Hospital; and Heather Urry, Ph.D., Associate Professor of Psychology, Tufts 
University.  Dr. Vangel was chosen for his expertise in statistical methods of 
neuroimaging analysis, and because, through his work as a biostatistician at the MGH 
and MIT General Clinical Research Centers, he has extensive experience in issues related 
to safe and ethical practices in biomedical research with human subjects.  Dr. Rauch was 
chosen for the DSMB because he has conducted extensive research on neuroimaging, 

 
 
17 
neurobiology, and neuromodulation of mood and anxiety disorders using modern 
technology, including brain stimulation and neurosurgery. Dr. Urry was chosen for the 
DSMB because she has extensive experience with fMRI and peripheral 
psychophysiology research. The DSMB will review the progress of the trial and safety of 
participants annually to discuss study progress, any safety concerns that have arisen, and 
make recommendations to improve safety procedures if indicated. The PIs will consult 
with the DSMB more frequently if safety issues arise or if it would be beneficial to obtain 
additional input from the DSMB. 
 
X.  REFERENCES 
 
1.Abramowitz JS. Effectiveness of psychological and pharmacological treatments for 
obsessive-compulsive disorder: a quantitative review of the controlled treatment 
literature Journal of Consulting and Clinical Psychology 1997, 65: 44–52. 
2.Abramowitz JS, Taylor S, McKay D: Potentials and limitations of cognitive 
treatments for obsessive-compulsive disorder. Cogn Behav Ther 2005, 34:140-
147.  
3.Berlim MT, Neufeld NH, Van den Eynde F. Repetitive transcranial magnetic 
stimulation (rTMS) for obsessive-compulsive disorder (OCD): an exploratory 
meta-analysis of randomized and sham-controlled trials. J Psychiatr Res. 2013 
Aug;47(8):999-1006. doi: 10.1016/j.jpsychires.2013.03.022. Epub 2013 Apr 21. 
Review. 
4.Bouton ME (2000) A learning theory perspective on lapse, relapse, and the 
maintenance of behavior change. Health Psychol 19: 57-63. 
5.Bouton ME, Bolles RC (1979) Role of conditioned contextual stimuli in 
reinstatement of extinguished fear. J Exp Psychol Anim Behav Process 5: 368-
378. 
6.Bouton ME, King DA (1983) Contextual control of the extinction of conditioned 
fear: tests for the associative value of the context. J Exp Psychol Anim Behav 
Process 9: 248-265. 
7.Bouton ME, Nelson JB, Rosas JM (1999) Stimulus generalization, context change, 
and forgetting. Psychol Bull 125: 171-186. 
8.Bremner JD (2002) Neuroimaging studies in post-traumatic stress disorder. Curr 
Psychiatry Rep 4: 254-263. 
9. Bush G, & Shin L M (2006) The Multi-Source Interference Task: an fMRI task 
that reliably activates the cingulo-frontal-parietal cognitive/attention network. 
Nature Protocols, 1(1), 308-313 

 
 
18 
10. Camprodon JA, Kaur N, Rauch SL, Dougherty DD. Neurotherapeutics. In: Stern 
TA, Rosenbaum JF, Fava M, Biederman J, Rauch SL (eds.) The Massachusetts 
General Hospital Comprehensive Clinical Psychiatry (2nd Edition). Philadelphia, 
PA: Elsevier. In press. 
 
11. Chen R, Lozano AM, Ashby P. Mechanism of the silent period following 
transcranial magnetic stimulation. Evidence from epidural recordings. Exp Brain 
Res 1999;128(4):539e42.  
12. Costa PT, McCrae RR. (1992) Revised NEO Personality Inventory (NEO-PI-R) 
and NEO five-factor inventory (NEO-FFI) Professional Manual. Odessa: 
Psychological Assessment Resources, Inc. 
13. Dale, A.M., Fischl, B., Sereno, M.I., 1999. Cortical surface-based analysis. I. 
Segmentation and surface reconstruction. Neuroimage 9, 179-194. 
 
14. Dale, A.M., Sereno, M.I., 1993. Improved localization of cortical activity by 
combining EEG and MEG with MRI cortical surface reconstruction: a linear 
approach. J Cogn Neurosci 5, 162-176. 
 
15. Davis M, Whalen PJ (2001) The amygdala: vigilance and emotion. Mol 
Psychiatry 6: 13-34. 
16. Desikan, R.S., Segonne, F., Fischl, B., Quinn, B.T., Dickerson, B.C., Blacker, D., 
Buckner, R.L., Dale, A.M., Maguire, R.P., Hyman, B.T., Albert, M.S., Killiany, 
R.J., 2006. An automated labeling system for subdividing the human cerebral 
cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968-
980. 
 
17. Estes WK, Skinner BF (1941) Some quantitative properties of anxiety. J Exp 
Psychol 29: 390-400. 
18. First MB, Spitzer RL, Gibbon M, Williams JBW (1990) Structured Clinical 
Interview for DSM-IV. New York: New York State Psychiatric Institute, 
Biometrics Research Department. 
19. Fischl, B., Dale, A.M., 2000. Measuring the thickness of the human cerebral 
cortex from magnetic resonance images. Proc Natl Acad Sci U S A 97, 11050-
11055. 
 
20. Fischl, B., Liu, A., Dale, A.M., 2001. Automated manifold surgery: constructing 
geometrically accurate and topologically correct models of the human cerebral 
cortex. IEEE Trans Med Imaging 20, 70-80. 
 
21. Fischl, B., Salat, D.H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van 
der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., Montillo, A., Makris, 

 
 
19 
N., Rosen, B., Dale, A.M., 2002. Whole brain segmentation: automated labeling 
of neuroanatomical structures in the human brain. Neuron 33, 341-355. 
 
22. Fischl, B., Salat, D.H., van der Kouwe, A.J., Makris, N., Segonne, F., Quinn, 
B.T., Dale, A.M., 2004a. Sequence-independent segmentation of magnetic 
resonance images. Neuroimage 23 Suppl 1, S69-84. 
 
23. Fischl, B., Sereno, M.I., Dale, A.M., 1999a. Cortical surface-based analysis. II: 
Inflation, flattening, and a surface-based coordinate system. Neuroimage 9, 195-
207. 
 
24. Fischl, B., Sereno, M.I., Tootell, R.B., Dale, A.M., 1999b. High-resolution 
intersubject averaging and a coordinate system for the cortical surface. Hum Brain 
Mapp 8, 272-284. 
 
25. Fischl, B., van der Kouwe, A., Destrieux, C., Halgren, E., Segonne, F., Salat, 
D.H., Busa, E., Seidman, L.J., Goldstein, J., Kennedy, D., Caviness, V., Makris, 
N., Rosen, B., Dale, A.M., 2004b. Automatically parcellating the human cerebral 
cortex. Cereb Cortex 14, 11-22. 
26. Foa, E.B., Huppert, J.D., Leiberg, S., Langner, R., Kitchik, R, Hajcak, G. & 
Salkovskis, P.M. (2002). The Obsessive-Compulsive Inventory: Development and 
Validation of a Short Version.  Psychological Assessment, 14, 485-496. 
27. Gillan, C. M., Otto, A. R., Phelps, E. A., & Daw, N. D. (2015). Model-based 
learning protects against forming habits. Cognitive, Affective, & Behavioral 
Neuroscience, 15(3), 523-536. 
 
28. Goodman WK, Price, LH, Rasmussen SA, Mazure, C et al. (1989). The Yale-
Brown Obsessive Compulsive Scale: I. Development, use, and reliability. Arch 
Gen Psych 46(11): 1006-1011. 
29. Goodman WK, Grice DE, Lapidus KA, Coffey BJ. Obsessive-Compulsive 
Disorder. 
Psychiatr Clin North Am. 2014 Sep;37(3):257-267. doi: 
10.1016/j.psc.2014.06.004.  
Epub 2014 Jul 23. Review. PubMed PMID: 25150561. 
30. Groppa S, Oliviero A, Eisen A, Quartarone A, Cohen LG, Mall V, Kaelin-Lang 
A, Mima T, Rossi S, Thickbroom GW, Rossini PM, Ziemann U, Valls-Solé J, 
Siebner HR. A practical guide to a diagnostic transcranial magnetic stimulation: 
report of an IFCN committee. Clin Neurophysiol. 2012 May: 123(5): 858-82. 
 
31. Han, X., Jovicich, J., Salat, D., van der Kouwe, A., Quinn, B., Czanner, S., Busa, 
E., Pacheco, J., Albert, M., Killiany, R., Maguire, P., Rosas, D., Makris, N., Dale, 
A., Dickerson, B., Fischl, B., 2006. Reliability of MRI-derived measurements of 
human cerebral cortical thickness: the effects of field strength, scanner upgrade 
and manufacturer. Neuroimage 32, 180-194. 

 
 
20 
 
32. Jovicich, J., Czanner, S., Greve, D., Haley, E., van der Kouwe, A., Gollub, R., 
Kennedy, D., Schmitt, F., Brown, G., Macfall, J., Fischl, B., Dale, A., 2006. 
Reliability in multi-site structural MRI studies: effects of gradient non-linearity 
correction on phantom and human data. Neuroimage 30, 436-443. 
 
33. Konorski J (1967) Integrative Activity of the Brain. Chicago: University of 
Chicago Press 
 
34. Kuperberg, G.R., Broome, M.R., McGuire, P.K., David, A.S., Eddy, M., Ozawa, 
F., Goff, D., West, W.C., Williams, S.C., van der Kouwe, A.J., Salat, D.H., Dale, 
A.M., Fischl, B., 2003. Regionally localized thinning of the cerebral cortex in 
schizophrenia. Arch Gen Psychiatry 60, 878-888. 
 
35. LeDoux JE (2000) Emotion circuits in the brain. Annu Rev Neurosci 23: 155-184. 
36. Mantovani A, Rossi S, Bassi BD, Simpson HB, Fallon BA, Lisanby SH. 
      Modulation of motor cortex excitability in obsessive-compulsive disorder: an  
exploratory study on the relations of neurophysiology measures with clinical 
outcome. Psychiatry Res. 2013 Dec 30;210(3):1026-32. 
 
37. Meyer BU, Roricht S, Grafin von Einsiedel H, Kruggel F, Weindl A. Inhibitory 
and excitatory interhemispheric transfers between motor cortical areas in normal 
humans and patients with abnormalities of the corpus callosum. Brain 
1995;118(Pt 2):429e40.  
 
38. National Institute for Health and Clinical Excellence (NICE): Core Interventions 
in the Treatment of OCD and BDD. London: BPS/Royal College of Psychiatrists; 
2006.  
 
39. Olatunji BO, Davis ML, Powers MB, Smits JAJ: Cognitive-behavioral therapy for 
obsessive-compulsive disorder: A meta-analysis of treatment outcome and 
moderators.J Psychiatr Res 2013, 47:33-41.  
 
40. O'Reardon JP, Solvason HB, Janicak PG,, et al. Efficacy and safety of transcranial 
magnetic stimulation in the acute treatment of major depression: a multisite 
randomized controlled trial. Biol Psychiatry. 2007; 62(11):1208-1216. 
 
41. Orr SP, Metzger LJ, Lasko NB, Macklin ML, Peri T, Pitman RK (2000) De novo 
conditioning in trauma-exposed individuals with and without posttraumatic stress 
disorder. J Abnorm Psychol 109: 290-298. 
42. Paulus W, Classen J, Cohen LG, Large CH, Di Lazzaro V, Nitsche M, et al. State 
of the art: pharmacologic effects on cortical excitability measures tested by 
transcranial magnetic stimulation. Brain Stimul 2008;1(3):151e63. 
 

 
 
21 
43. Pavlov I (1927) Conditioned Reflexes. London: Oxford University Press. 
44. Pitman RK, Shin LM, Rauch SL (2001) Investigating the pathogenesis of 
posttraumatic stress disorder with neuroimaging. J Clin Psychiatry 62 Suppl 17: 
47-54. 
45. Ponniah K, Magiati I, Hollon SD: An update on the efficacy of psychological 
treatments for obsessive-compulsive disorder in adults. J Obsessive-Compuls 
Relat Disord 2013, 2:207-218.  
46. Quirk, G. J. and Gehlert, D. R. Inhibition of the amygdala: Key to pathological 
states? Ann.N.Y.Acad.Sci.  2002.  
 
47. Rescorla RA, Heth CD (1975) Reinstatement of fear to an extinguished 
conditioned stimulus. J Exp Psychol Anim Behav Process 1: 88-96. 
 
48. Reuter, M., Schmansky, N.J., Rosas, H.D., Fischl, B. 2012. Within-Subject 
Template Estimation for Unbiased Longitudinal Image Analysis. Neuroimage, in 
print. http://dx.doi.org/10.1016/j.neuroimage.2012.02.084 
49. Reuter, M., Fischl, B., 2011. Avoiding asymmetry-induced bias in longitudinal 
image processing. Neuroimage 57 (1), 19-21. http://reuter.mit.edu/papers/reuter-
bias11.pdf 
 
50. Reuter, M., Rosas, H.D., Fischl, B., 2010. Highly Accurate Inverse Consistent 
Registration: A Robust Approach. Neuroimage 53 (4), 1181–1196. 
http://reuter.mit.edu/papers/reuter-robreg10.pdf 
 
51. Rosa-Alcázar AI, Sánchez-Meca J, Gómez-Conesa A., Marín-Martínez F. 
Psychological treatment of obsessive-compulsive disorder: a meta-analysis 
Clinical Psychology Review 2008, 28: 1310–1325. 
 
52. Rosas, H.D., Liu, A.K., Hersch, S., Glessner, M., Ferrante, R.J., Salat, D.H., van 
der Kouwe, A., Jenkins, B.G., Dale, A.M., Fischl, B., 2002. Regional and 
progressive thinning of the cortical ribbon in Huntington's disease. Neurology 58, 
695-701. 
 
53. Ruscio AM, Stein DJ, Chiu WT, Kessler RC. The epidemiology of obsessive-
compulsive disorder in the National Comorbidity Survey Replication. Mol 
Psychiatry. 2010;15(1):53. 
 
54. Rossi S, Hallett M, Rossini PM, Pascual-Leone A. Safety, ethical considerations, 
and application guidelines for the use of transcranial magnetic stimulation in 
clinical practice and research. Clin Neurophysiol. 2009; 120(12):2008-2039. Epub 
2009/10/17. 
 

 
 
22 
55. Rush AJJ, First MB, Blacker D. Handbook of Psychiatric Measures, Second 
Edition. Washington, D.C.: American Psychiatric Publishing, Inc.; 2008. 
56. Salat, D.H., Buckner, R.L., Snyder, A.Z., Greve, D.N., Desikan, R.S., Busa, E., 
Morris, J.C., Dale, A.M., Fischl, B., 2004. Thinning of the cerebral cortex in 
aging. Cereb Cortex 14, 721-730. 
 
57. Segonne, F., Dale, A.M., Busa, E., Glessner, M., Salat, D., Hahn, H.K., Fischl, B., 
2004. A hybrid approach to the skull stripping problem in MRI. Neuroimage 22, 
1060-1075. 
 
58. Segonne, F., Pacheco, J., Fischl, B., 2007. Geometrically accurate topology-
correction of cortical surfaces using nonseparating loops. IEEE Trans Med 
Imaging 26, 518-529. 
 
59. Sled, J.G., Zijdenbos, A.P., Evans, A.C., 1998. A nonparametric method for 
automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med 
Imaging 17, 87-97. 
 
60. Steketee G, Frost R. (2001). Development and initial validation of the Obsessive 
Beliefs Questionnaire and the Interpretation of Intrusions Inventory. Behaviour 
Research and Therapy 39(8): 987-1006. 
61. Spielberger CD, Gorsuch RL, Lushene RE. (1990) Manual for the State-Trait 
Anxiety   Inventory (Self-Evaluation Questionnaire). Palo Alto: Consulting 
Psychologists Press. 
62. Wilhelm S, Steketee G, Fama JM, Buhlmann U, Teachman B, Golan E. Modular 
cognitive therapy for obsessive-compulsive disorder: a wait-list controlled trial. 
Journal of Cognitive Psychotherapy 2009, 23: 294–305. 
 
63. Yang JC, Papadimitriou G, Eckbo R, Yeterian EH, Liang L, Dougherty DD, 
Bouix S, Rathi Y, Shenton M, Kubicki M, Eskandar EN, Makris N. Multi-tensor 
investigation of orbitofrontal cortex tracts affected in subcaudate tractotomy. 
Brain Imaging Behav. 2014 Aug 8. [Epub ahead of print] 
 
64. Ziemann U. TMS and drugs. Clin Neurophysiol. 2004 Aug;115(8):1717-29. 
Review.