A Randomized, Double-Blind Study of Neural Circuit Responses to COMT Inhibitors
Phase: NA
Conditions: Impulsive Behavior
Sponsor: University of California, San Francisco

 
Title of Study: A Study of Neural Circuit Responses to Catechol-O-methyl Transferase (COMT) 
Inhibitors 
 
Publication: 
Dopamine, Corticostriatal Connectivity, and Intertemporal Choice  
NCT01158950   
July 4 2012 
 
 

 
Materials and Methods 
 
Subject training and task performance 
We screened 45 subjects, of whom 27 healthy subjects (i.e. without a history of neurological or 
psychiatric illnesses) were eligible to participate.  All subjects gave written informed consent in 
accordance with the Committee for the Protection of Human Subjects at the University of 
California, San Francisco and University of California, Berkeley.  Subjects first underwent a 
history and physical exam, as well as blood testing for liver function, to ensure that there were no 
medical contraindications to tolcapone use or MRI scanning.  They then completed a number of 
screening questionnaires, including the Barratt Impulsivity Scale (BIS).  One subject was 
excluded prior to behavioral analysis for use of an albuterol inhaler immediately before MRI 
scanning.  Three subsequent subjects were excluded after task performance: one reported an 
explicit change in discounting strategy prior to the second session, one was noted to be 
significantly sleep-deprived after completing final exams the day of the second session, and a 
third was a non-native English speaker who may have misunderstood task instructions.  The 
differential impulsive choice ratio (ICR; see below) of the first of these excluded subjects 
favored tolcapone (-0.59) and of the other two favored placebo (0.45, 0.92); all values were 
identified as significant outliers (p < 0.005) by Rosner’s generalized extreme studentized deviate 
(ESD) procedure (Rosner, 1983).  Of the 23 subjects who remained, ages ranged from 19-41 
years old; 13 were female.  All subjects had normal neuroanatomy as reviewed by a neurologist 
(A.S.K.), were right-handed, and had normal or corrected-to-normal vision. Before scan sessions, 
subjects were trained on the delay discounting task to familiarize them with task procedures. 
Subjects then underwent two 2.25-hour fMRI sessions consisting of 8 runs of 32 task trials, for a 

total of 256 trials, and two resting state runs.  All subjects were compensated for their 
participation. 
 
Experimental paradigm 
Subjects were randomized in double-blind, counterbalanced, placebo-controlled fashion to either 
placebo or a single 200mg dose of tolcapone on their first visit and the alternative treatment on 
their second visit. After receiving task instructions and undergoing a brief practice session of 10-
20 trials, subjects performed the delay discounting task (Figure 1) within the MRI scanner while 
blood-oxygen level dependent (BOLD) images were obtained.  Subjects entered the MRI scanner 
90 minutes after drug ingestion to ensure that the delay discounting task was performed while 
drug levels were at peak (approximately 120 minutes: tolcapone package insert, Valeant 
Pharmaceuticals).  The 256 total trials were presented in pseudorandom order.  At the start of 
each trial, subjects were cued to one of four trial types:  Want (W), Don’t Want (DW), Sooner 
(S), and Larger (L) (see below).  For each of these trial types, subjects were then presented with 
two hypothetical alternatives: a smaller amount of money available today (“Now”) and a larger 
amount available later (“Later”).  We have previously shown that this paradigm with 
hypothetical rewards effectively engages subjects (Boettiger et al., 2007; Boettiger et al., 2009), 
consistent with reports that hypothetical rewards activate common brain regions involved in 
value computations (Kang et al., 2011).  As in our previous work (Boettiger et al., 2007), the 
‘Later’ option consisted of six amounts ($1, $2, $5, $10, $20, or $100) at one of five future 
delays (1 week, 2 weeks, 1 month, 3 months, or 6 months).  The percentage difference between 
the Now and Later options was selected from one of four different values (30%, 15%, 10%, and 
5%).  Subjects then made a button press to select one of the two options, randomly assigned to 

the left and right sides of the screen.   
 
Each of the four trial types allowed us to investigate different functions.  In the W condition, the 
primary analytic focus of this study, subjects chose the option they preferred.  In the DW 
condition, subjects also chose the option they preferred, but then made a button-press to select 
the opposite choice.  This condition permitted us to evaluate motor impulsivity by comparing the 
proportion of responses in the W and DW conditions.  In the S and L conditions, which we 
combined to form a control (CON) condition, subjects simply selected the sooner or larger 
options, respectively.  These trial types allowed us to ensure that subjects were appropriately 
following instructions, and to introduce a condition in which the decision about monetary options 
was not a motivated choice.  The W condition comprised half of all trials; the CON conditions 
comprised one-third; and the DW condition comprised one-sixth. As expected, subjects 
performed very well in the CON condition (accuracy = 0.97  0.008 (sem)) and therefore we do 
not further report results of the CON condition in this paper. 
 
The primary behavioral outcome was the impulsive choice ratio (ICR), which represents the ratio 
of the number of sooner choices to the number of total choices in the W condition.  As in our 
previous work, we calculated a number of related measures of impulsive choice, including a 
measure of the hyperbolic discounting rate (k) and the interest rate differentiating the two 
choices. Because values such as the hyperbolic discounting rate were highly correlated with ICR 
(e.g. r (ICR, k) = 0.78, p < 1 x 10-5) and consequently gave qualitatively similar results, we 
elected to study the simplest, most intuitive quantity, as in our previous work (Boettiger et al., 

2007; Boettiger et al., 2009).  ICR values underwent an arcsine-square root transform – i.e. were 
variance-stabilized – to permit the application of parametric statistical tests. 
 
Both before drug administration and after the scanner run, subjects completed a speeded 
responding task to assess potential changes in motor function on and off tolcapone.  Subjects 
were required to make a button press response as soon as possible after the presentation of either 
a brief visual or auditory stimulus.  Reaction times were compared both within each session and 
across the tolcapone and placebo conditions.  Due to computer failures, these data were available 
for only 19 of our 23 subjects. 
 
MRI image acquisition 
MRI scanning was conducted on a Siemens MAGNETOM Trio 3T MR Scanner at the Henry H. 
Wheeler, Jr. Brain Imaging Center at the University of California, Berkeley.  Anatomical images 
consisted of 160 slices acquired using a T1-weighted MP-RAGE protocol (TR = 2300 ms, TE = 
2.98 ms, FOV = 256 mm, matrix size = 256 x 256, voxel size = 1 mm3).  Functional images 
consisted of 24 slices acquired with a gradient echoplanar imaging protocol (TR = 1370 ms, TE 
= 27 ms, FOV = 225 mm, matrix size = 96 x 96, voxel size = 2.3 x 2.3 x 3.5 mm). A projector 
(Avotec SV-6011, http://www.avotec.org) was used to display the image on a translucent screen 
placed within the scanner bore behind the head coil.  A mirror was used to allow the subject to 
see the display. Subjects made their responses via an MRI-safe fiber optic response pad (Inline 
Model HH-1x4-L, http://www.crsltd.com). 
 
fMRI preprocessing 

fMRI preprocessing was performed using both the AFNI (http://afni.nimh.nih.gov) and FSL 
(http://www.fmrib.ox.ac.uk/fsl/) software packages.  Functional images were converted to 4D 
NIfTI format and corrected for slice-timing offsets.  Motion correction was carried out using the 
AFNI program 3dvolreg, with the reference volume set to the mean image of the first run in the 
series. Images were then smoothed with a 5mm FWHM Gaussian kernel. Co-registration was 
performed with the AFNI program 3dAllineate using the local Pearson correlation cost function 
optimized for fMRI-to-MRI structural alignment. The subsequent inverse transformation was 
used to warp the anatomical image to the functional image space.  Anatomical images were 
normalized to a standard volume (MNI_N27) using the FSL program fnirt available from the 
Montreal Neurological Institute (MNI; http://www.bic.mni.mcgill.ca).  The same normalization 
parameters were later applied to native-space statistical maps as necessary for the generation of 
group statistical maps (see below). 
 
Univariate analysis 
To address a series of hypotheses, we carried out a number of voxel-wise fMRI statistical 
analyses for each subject using the general linear model (GLM) framework implemented in the 
AFNI program 3dDeconvolve. The BOLD correlates of different decisions were assessed by 
modeling the cue and decision phases of the task for the four different conditions (Want, Don’t 
Want, Sooner, Larger) with separate regressors, each of which was derived by convolving a 
gamma probability density function (peaking at 6 seconds) with a vector of stimulus onsets for 
each condition.  Map-wise significance (p < 0.05, corrected for multiple comparisons) was 
determined by applying a cluster-size correction derived from the AFNI programs 3dFWHMx 
and 3dClustSim on data initially thresholded at a value of p < 0.005 (uncorrected).  For the main 

effect of task (figure 3), a whole-brain correction was applied. This contrast compared event 
onsets from all conditions – including both cue and outcome – with baseline fMRI activity on 
tolcapone versus placebo to illustrate that there are widespread changes in BOLD signal across 
the brain resulting from the use of tolcapone.   Because cortical dopamine projections are 
frontally predominant (Cools, 2008), subsequent univariate analyses designed to address more 
specific task-related hypotheses about changes in frontostriatal regions used the AAL template 
brain (Tzourio-Mazoyer et al., 2002) to generate a frontostriatal mask (AAL areas 3-32 and 71-
76; analyses illustrated in figures 4 and 5a).  
 
Multivariate analysis 
Resting state data were used to evaluate the connectivity between brain regions in both the 
tolcapone and placebo conditions in all subjects who completed both resting state sessions (N = 
22; due to technical failures, complete resting state data from one of our 23 subjects were not 
obtained).  This approach allowed us to identify any drug-related changes in connectivity 
independent of task performance.  These data were smoothed by a 2.5mm FWHM Gaussian 
kernel prior to temporal bandpass filtering between 0.009 Hz and 0.08 Hz to reduce the influence 
of cardiac and respiratory artifact, per the protocol of Fox and colleagues (Fox et al., 2005).  
Movement parameters and the white matter and ventricular time series, but not the global mean 
signal, were included as regressors of no interest.  A striatal region of interest in the left ventral 
putamen was chosen, based on the results of our univariate analyses (see Results, Figure 4a).  A 
time course defined by averaging across voxels in this region was then correlated with every 
other voxel in the brain, and correlation coefficients were Fisher-transformed to allow for the 
application of parametric statistical tests.  Images were then normalized to the MNI template 

prior to the application of group-level statistics.  Because of our hypotheses about changes in 
frontostriatal connectivity, data were masked with a prefrontal mask (AAL regions 3-32) before 
the appropriate cluster-size correction was calculated. 
 
For the evaluation of connectivity during task performance, we used the seed region (left ventral 
putamen) and significantly-connected region (left pregenual anterior cingulate cortex) identified 
in the resting state connectivity analysis shown in Figure 5b.  For each of these two ROIs, the 
time series for each of the eight task-state runs was windowed with a 4-point split-cosine bell and 
concatenated with the other segments to produce a subject-specific time series. Time series 
within each ROI were then averaged across voxels to generate a single time series for each ROI.  
Coherency values were obtained by applying a fast Fourier transform (Matlab, 
http://www.mathworks.com) to the data for each pair of ROIs, implemented via Welch’s 
periodogram averaging method using a 64-point discrete Fourier transform, Hanning window, 
and overlap of 32 points (Kayser et al., 2009). Coherence values for each ROI were then 
computed using the band-averaged coherence.  To compute correlations between coherence 
results and other values, we first Fisher-transformed the coherence values to generate an 
approximately normal distribution (Rosenberg et al., 1989) that permitted us to apply parametric 
statistical tests. 
 
Statistical Analysis 
For analysis of behavioral data, paired T-tests and Pearson’s correlation coefficients were used to 
calculate statistical significance.  For univariate and multivariate analyses of BOLD data, 
signficance was calculated using statistical techniques and corrections implemented in the Afni 

software package, including the functions 3dDeconvolve, 3dFWHMx, 3dClustSim, and 
3dttest++.