Journal of Behavioral and Brain Science, 2011, 1, 124-133
doi:10.4236/jbbs.2011.13017 Published Online August 2011 (http://www.SciRP.org/journal/jbbs)
Impact of Marijuana on Response Inhibition: An fMRI
Study in Young Adults
Andra M. Smith1*, Rocío A. López Zunini1, Christopher D. Anderson1, Carmelinda A. Longo1,
Ian Cameron2, Matthew J. Hogan2, Peter A. Fried3
1School of Psychology, University of Ottawa, Ottawa, Canada
2The Ottawa Hospital, Department of Diagnostic Imaging, Ottawa, Canada
3Department of Psycholo gy , Carleton University, Ottawa, Canada
Received June 26, 2011; revised July 19, 2011; accepted August 4, 2011
Abstract
Rationale: Marijuana use in adolescence is prevalent and increasing. Understanding the neural correlates of
the impact of this use is critical for policy making and for youth awareness. Objectives: The effects of mari-
juana use on response inhibition were investigated in 19 - 21-year-olds using functional magnetic resonance
imaging (fMRI). Methods: Participants were members of the Ottawa Prenatal Prospective Study, a longitu-
dinal study that collected a unique body of information on participants from infancy to young adulthood in-
cluding: prenatal drug history, detailed cognitive/behavioral performance, and current and past drug use.
This information allowed for the control of an unparalleled number of potentially confounding variables in-
cluding: prenatal marijuana, nicotine, alcohol, and caffeine exposure and offspring alcohol, marijuana, and
nicotine use. Ten marijuana users and 14 nonusers that served as controls performed a Go/No-Go task while
fMRI blood oxygen level-dependent response was examined. Results: Despite similar task performance,
there was a positive relationship between amount of marijuana smoked and activation in right thalamus,
premotor cortex and middle frontal gyrus. These regions form part of the neural network responsible for in-
hibition control. There was also a positive dose dependent relationship with marijuana and activation in infe-
rior parietal lobe and precuneus, also parts of response inhibition pathways. Conclusions: These results sug-
gest a dose dependent alteration in neural functioning during response inhibition after controlling for other
prenatal and current drug use. These alterations may be necessary in order to compensate for neural changes
in response inhibition circuits caused by long term marijuana use that began during adolescence/young adult-
hood.
Keywords: Prefrontal Cortex, fMRI, Marijuana, Young Adulthood, Response Inhibition
1. Introduction
Research has demonstrated that the inability to success-
fully monitor and inhibit inappropriate behaviours is ap-
parent in substance abusers as well as in other individu-
als with altered frontal neural circuitry [1]. Such disrup-
tion in executive functioning, which can also include
selective attention and short term storage of information,
initiation of response to relevant information and self-
monitoring of performance in order to achieve a desired
goal [1], can cause severe disruption in daily life. Of these
elements, however, response inhibition is most vital since
it allows for successful adaptation to the environment,
recognizing unexpected situations, making plans and
changing behaviour accordingly.
Functional magnetic resonance imaging (fMRI) re-
search has shown that response inhibition is mediated by
a wide neural network that involves the frontal lobes as
well as circuits connecting the frontal lobes with other
regions such as the parietal lobes, cerebellum, striatum
and thalamus [2-3]. Other observed regions include the
premotor area, the supplementary motor area, the dorso-
lateral and orbitofrontal areas and the anterior cingulate
cortex [4].
The 2011 Monitoring the Future Survey reported that
there is an increase in American youth marijuana use and

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125
that there has been an attenuation of perceived risks as-
sociated with regular marijuana use [5]. These trends
highlight the importance of understanding the impact of
marijuana on neural processing.
Using fMRI Tapert et al. [6] compared adolescent
marijuana users and nonusers during a Go/No-Go task
and found that users showed altered blood oxygen level
dependent (BOLD) response during both Go and No-Go
trials even after 28 days of abstinence. Users showed
greater activation prominently in the dorsolateral pre-
frontal cortex and the parietal cortex. This was interpreted
as an increase in effort required to perform the task. Ad-
ditionally, using a Stroop test, a measure of response in-
hibition, and fMRI, [1] compared adult marijuana users
and nonusers, with the users testing positive for recent
marijuana use in a urine test. Consistent with [6], they
found greater activation, in the users compared to nonus-
ers, in the dorsolateral prefrontal cortex. In addition, they
also found that users showed decreased activity in the
anterior cingulate cortex. These results were interpreted
to suggest that the marijuana smokers used different cor-
tical processes than nonusers to perform the task. In two
more recent studies, results also illustrated that active
marijuana users display greater levels of functional ab-
normalities than abstinent users in frontal, parietal and
cerebellar brain regions as they performed other executive
functioning tasks, including visuospatial working mem-
ory [7-9]. Again, similar interpretations were suggested,
including that marijuana users were required to recruit
different neural pathways to perform the tasks and that
exposure at a young age may increase the vulnerability to
these effects. Despite these findings, further data is needed
to more clearly specify and elaborate how early exposure
to marijuana affects neural processing in young adult-
hood. An important requisite in this quest is a well con-
trolled sample.
The differences in neural activation in marijuana users
are mostly due to delta-9-tetrahydrocannabinol (THC),
marijuana’s most active psychoactive ingredient, which
acts as a ligand for human cannabinoid receptors. The
wide distribution of these receptors in the human brain,
with particularly high densities in the cerebellum, parts
of the basal ganglia, hippocampus, and many regions of
the neocortex, poses great concern for the maintenance
of a healthy ability to cognitively process information [3].
Considering the neurodevelopment that occurs during
adolescence and young adulthood, specifically, prefron-
tal cortex development and the subsequent advancement
of executive functioning, it is clear that understanding
this neural impact of marijuana in youth is imperative.
Further understanding this impact was the goal of the
present study.
The Ottawa Prenatal Prospective Study (OPPS) is an
ongoing longitudinal investigation that was initiated in
1978, with the primary objective of examining the effects
of “soft” prenatal drug exposure on offspring. Children
were followed from infancy to young adulthood where
detailed information has been collected on their prenatal
drug exposure, current and past drug use, cognitive/be-
havioral performance, and over 4000 lifestyle variables
[10-14]. Using this unique sample in combination with
the powerful imaging technique, fMRI, and a well estab-
lished Go/No Go task, the purpose of the present study
was to determine if there was a significant relationship
between brain activity and marijuana use and if this could
be observed in young adults with relatively few years of
exposure. Based on previous research where marijuana
users and nonusers showed no differences in task per-
formance [6,8], it was hypothesized that there would be
no performance differences between groups for the pre-
sent study. Despite this, marijuana users would require
greater activation than controls in brain regions that typi-
cally demonstrate response inhibition in order to suc-
cessfully perform the task, including the prefrontal cor-
tex.
2. Methods
2.1. Participants
Participants were recruited from the OPPS and signed an
informed consent before participation in the study. This
study was approved by The Ottawa Hospital ethics board
in agreement with the ethical standards laid down in the
1964 Declaration of Helsinki. The sample consisted of
ten marijuana users (six males, four females, mean age
20, range of ages 19 - 21) and 14 non-using controls
(nine males, five females, mean age of 20, range of ages
19 - 21). Current marijuana use was defined as regular
use of marijuana cigarettes per week (>1 joint per week).
The users reported smoking an average of 11.48 mari-
juana joints per week (range of 2 - 37.5 joints per week)
on a regular basis and had been smoking marijuana for
an average of 4.55 years. The lifetime use for this group
would approximate an average of 2697 joints smoked.
Previous studies have considered 180 - 1844 lifetime
consumption of marijuana as heavy exposure [15]. The
nonusers reported never using marijuana regularly. Spo-
radic marijuana use was reported by three of the 14 con-
trols but no more than one to four times in the past year.
No participants had used other illicit drugs on a regular
basis or within a month before testing. The illicit drug
categories included were amphetamines, crack, cocaine,
heroin, mushrooms, hashish, lysergic acid, steroids, sol-
vents, and tranquilizers. Seven of the ten marijuana users
smoked nicotine cigarettes on a regular basis while no

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126
participants from the nonusers control group smoked
cigarettes on a regular basis. Cigarette use has been con-
trolled for in the statistical analysis.
Participants from both groups were between the ages
of 19 and 21, were right handed, had English as his/her
first language and were from middle-class homes. No
parents of the participants were reported to have an Axis
I diagnosis from the Diagnostic and Statistical Manual of
Mental Disorders DSM-IV [16]. Participants completed a
comprehensive psychological battery including, the
Wechsler Adult Intelligence Scale-III [17], the NEO
Personality Inventory [18], and Computerized Diagnostic
Interview Schedule for Children (C-DISC) [19], which
assessed current psychiatric illness based on DSM-IV
criteria. Parents also previously completed the Conners’
Parent Rating Scale [20] and provided information on
socioeconomic status. No significant differences were
found between current marijuana users and nonusers on
these scales. Thus, they were not included in the fMRI
analyses (see Table 1).
Participants completed a self-report drug questionnaire
following the fMRI session. The questionnaire requested
information on current and past marijuana use, as well as
all other drug use (illicit or non illicit). Participants were
asked to abstain from drug use 1 - 2 hours prior to testing.
They were required to provide a urine sample upon arri-
val at the MRI unit which was sampled for cannabis,
amphetamines, opiates, cocaine, creatinine, and cotinine.
All metabolite concentrations were adjusted for create-
nine to control for urine dilution. Significant differences
were found between groups for current nicotine and al-
cohol use and again, have been addressed in the statisti-
cal analyses below. Exclusion criteria included (a) diag-
nosis of DSM-IV Axis I disorder using the C-DISC; (b)
positive urine tests for cocaine, opiates, or amphetamines
or self-reported regular use of any of these drugs (de-
fined as once/month or more); (c) contraindication to
MRI/fMRI (for example a pacemaker, metal implants,
accidents leaving metal in eyes, recent surgery, metal
dental work (aside from fillings), or insufficient vision
for viewing the task); or (d) any abnormalities in struc-
tural MRI scans.
Due to previous findings of the impact of prenatal ex-
posure to marijuana [21,22], it was important to consider
prenatal exposures. This was one of the benefits of using
the OPPS as this information was available for all par-
ticipants. Detailed information about participant’s prena-
tal drug exposure was previously gathered [10] and these
plus the offspring current use details are provided in Ta-
ble 2.
Table 1. Environmental and IQ variables for current marijuana users and non using controls.
Variable Current marijuana users
(n = 10, mean(SE))
Nonusers controls
(n = 14, mean (SE)) Results (ANOVA)
Family income 31,610 (5367.65) 31,611 (4707.74) F(1,21) = 0.00 (p < 0.99)
WAIS verbal IQ 106.10 (4.10) 116.53 (3.60) F(1,21) = 3.66 (p < 0.07)
NEO neuroticism 44.50 (15.87) 46.00 (8.00) F(1,18) = 0.08 (p < 0.79)
NEO extraversion 49.50 (17.85) 59.33 (7.44) F(1,18) = 2.94 (p < 0.10)
NEO openness 49.88 (10.90) 57.33 (11.50) F(1,18) = 2.10 (p < 0.16)
NEO agreeableness 45.88 (11.40) 54.75 (12.60) F(1,18) = 2.55 (p < 0.13)
NEO conscientiousness 46.75 (13.97) 54.92 (13.79) F(1,18) = 1.67 (p < 0.21)
Connor’s (learning problems) 0.17 (2.91) −0.50 (2.42) F(1,20) = 0.36 (p < 0.55)
Connor’s (anxiety) −0.26 (0.34) 0.30 (1.13) F(1,20) = 1.87 (p < 0.19)
No significant differences were observed between the groups for any variable.
Table 2. Drug exposure for marijuana users and nonusers controls.
Drug exposure Current marijuana users
(n = 10, mean(SE))
Nonusers
(n = 14, mean (SE)) Results (MANOVA)
Prenatal marijuana (joints/week) 8.82 (3.4) 1.12 (2.87) F(1,22) = 2.99 (p < 0.10)
Prenatal nicotine (cigarettes/day) 10.41 (3.15) 3.09 (2.66) F(1,22) = 3.14 (p < 0.09)
Current nicotine (cigarettes/day) 7.75 (1.29) 0.00 (1.09) F(1,22) = 20.91 (p < 0.001)
Current alcohol (drink/week) 4.77 (1.02) 2.00 (0.86) F(1,22) = 4.48 (p < 0.05)
Prenatal alcohol (AA/day) 0.13 (0.10) 0.28 (0.08) F(1,22) = 1.41 (p < 0.25)

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2.2. Image Acquisition
All imaging was performed using a 1.5 Tesla Siemens
Magnetom Symphony MR scanner with the quantum
gradient set (maximum amplitude = 30 mT/m and slew
rate = 125 T/m/s). Subjects lay supine with their head
secured in a standard MRI head holder. A conventional
T1-weighted spin echo localizer was acquired and used
to align the slice orientation for the fMRI scans. This
localizer was also used to prescribe a subsequent three-
dimensional FLASH (TR/TE 11.2/21 ms, flip angle 60˚,
field of view (FOV) 26 × 26 cm2, 256 × 256 matrix, slice
thickness 1.5 mm) volume acquisition used for further
structural analyses. Whole brain fMRI was performed
using a T2*-weighted echo planar pulse sequence (TR/TE
3,000/40 ms, flip angle 90˚, FOV 24 × 24 cm2, 64 × 64
matrix, slice thickness 5 mm, 27 axial slices, bandwidth
62.5 kHz).
2.3. Procedures
The cognitive task was presented to the participants on a
back projection screen, located at the foot of the patient
table, via a mirror attached to the head coil. All lighting
in the scanning room was turned off. Button-press re-
sponses were recorded via a MRI-compatible fiber optic
device (Lightwave Medical, Vancouver, British Colum-
bia, Canada). The stimuli were presented as white letter
on a black screen. Participants were asked to press as
quickly and accurately as possible and if they made a
mistake, to continue without thinking about it. The scan-
ning session began with an initial rest epoch of 9 s to
allow longitudinal magnetic relaxation (T1 effects) to
stabilize.
2.4. Go/No-Go Task
The Go/No-Go blocked design procedure involved pres-
entation of white letters, one at a time, on a black screen
for a period of 75 ms, with an inter-stimulus interval of
925 ms. Fifty percent of the stimuli were “X” and the
other 50% were other capital letters randomly selected
from the remainder of the alphabet. X and non-X stimuli
were presented in random order and each epoch was dif-
ferent with respect to order of stimulus presentation.
There were two types of conditions. In the “Press for X”
condition, participants were instructed to press a button
with the right index finger when an X was presented, and
refrain from pressing for all other letters. In the “Press
for all letters except X” condition, participants were in-
structed to refrain from pressing for X and to press for all
other letters with the right index finger. Both conditions
were presented in epochs of 27 s duration, including 3 s
of instructions and 24 stimuli (12 Go and 12 No-Go
stimuli). Each Go/No-Go epoch was followed by a 24 s
rest epoch. During instruction epochs, the instruction
“Press for X” or “Press for all letters except X” was pre-
sented on the screen. During rest epochs, the word REST
was presented and the participant was not required to
make any motor response. Participants performed a prac-
tice session of 10 trials of each Go/No-Go condition out-
side the scanning room. Within the scanning session,
there were four respond to X and four respond to non-X
epochs, presented in a counterbalanced order, always
starting with respond to X.
2.5. Performance Parameters and Analyses
Commission errors included any response following a
No-Go stimulus (e.g., pressing the button for stimulus B
in the ‘Press for X’ condition and pressing the button for
stimulus X in the “Press for all letters except X” condi-
tion) within 900 ms of stimulus presentation. Omission
errors were defined as a failure to respond to a target
stimulus within 900 ms. Mean reaction times were cal-
culated for both the “Press for X” and the “Press for all
letters except X” conditions for all accurate responses
occurring within 900 ms of stimulus presentation. The
behavioral data were analyzed using an ANCOVA with
nicotine and alcohol use as covariates. Separate AN-
COVAs were performed on the reaction time data, com-
mission errors and omission errors.
Confirming that the non-X condition involved more
response inhibition than the X condition, the reaction
time for the correct responses was longer and the errors
of commission were more frequent in the “Press for all
letter except X” condition than in the “Press for X” condi-
tion. This is consistent with the prediction that withholding
responding to a target (X in this task) placed greater de-
mand on the neural mechanism for response inhibition. As
both conditions entail similar sensory and motor proc-
essing, it is then possible to subtract the images for the
respond to X condition from those for the respond to
non-X condition to reveal the neural activity related to
response inhibition.
2.6. Image Processing and Analyses
Prior to statistical analyses, functional images from the
first 9 s of the initial rest block were discarded to ensure
that longitudinal magnetic relaxation (T1 effects) had
stabilized.
The remaining functional images were realigned to
correct for motion by employing the procedures of Fris-
ton et al. [23], using Statistical Parametric Mapping
(SPM8) software. The motion correction did not exceed

A. M. SMITH ET AL.
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1 mm for any subject. Images were spatially normalized
to match the echo planar imaging template provided in
SPM8. Following spatial normalization, images were
smoothed with an 8-mm full-width at half-maximum
Gaussian filter.
2.7. Imaging Whole Brain Analysis
All image analyses were performed using SPM8. Indi-
vidual participant fixed effects analyses were carried out
for the comparison of the “Press for all letter except for
X” condition minus the “Press for X” condition. In addi-
tion, individual fixed effect analyses were carried out for
the comparison of the “Press for all letters except for X”
condition minus the “Rest” condition. One contrast image
was created per person for each of the comparisons, and
these images were then used for second-level random
effects analyses. Group comparisons were performed
using both 2 sample t-tests and multiple regressions. Due
to the availability of information on each participant’s
drug use history and exposure, as well as other lifestyle
variables, comparisons between the marijuana users and
nonusers were performed using several second level
analyses, including multiple regression analyses with co-
variates specified for each.
3. Results
3.1. Drug Questionnaire and Urine Sample Data
All marijuana users had smoked marijuana during the
week of fMRI testing, with four of the ten participants
smoking marijuana on the day of testing (two partici-
pants smoked one joint in the morning while two smoked
throughout the day as was typical for their regular
use―but not within 3 hours of the testing session). The
average urine cannabis at the time of testing for the
group of ten using participants was 460 μg/L, with a
range from 16 to 1325 μg/L (all 10 had cannabis in their
urine). One of the nonusers had smoked one joint 3 days
prior to testing and had 45 μg/L in his urine; no other
exposure was reported for this participant in the months
prior to testing. No other nonuser had cannabis in their
urine. The average number of joints smoked by the mari-
juana users for the 7 days prior to testing was 4.2, 4.55,
3.15, 2.75, 2.9, 4.6, and 4.35, and on the day of testing,
the average use was 2.5 joints.
The cotinine values for urine samples revealed an av-
erage value of 888 μg/L for the marijuana using group
(seven of ten were cigarette smokers) while only 9.8 μg/L
for the nonusers group (which may be due to second
hand smoke exposure as none of the marijuana nonusers
smoked cigarettes on a regular basis). Thus, there were
significant differences between groups for nicotine use.
Therefore, amount of nicotine smoked/day was used as a
covariate in whole brain statistical analyses.
The Pearson correlation between the drug question-
naire results and the urine samples for levels of mari-
juana use was 0.97 (p < 0.001) while that for nicotine
(cotinine/creatinine) was 0.91 (p < 0.001). This high con-
cordance validated the use of the self-report drug ques-
tionnaire results for current use and drug history.
No participant from either group reported alcohol con-
sumption on the day of imaging. One of the marijuana
users reported drinking 15 alcoholic drinks on the day
prior to testing but no other participant reported more
than seven drinks for the 2 days prior to testing. This
reduces the possibility that the results were related to the
acute effects of alcohol consumption, given its short half
life.
3.2. Performance Data
There were no significant performance differences be-
tween marijuana users and nonusers on reaction time,
commission errors and omission errors while controlling
for nicotine and alcohol use (Table 3).
3.3. Whole Brain fMRI Analyses
Fixed effects analyses revealed an expected pattern of
activation from the non marijuana using participants.
Table 3. Performance data for the two conditions of the Go/No-Go task for marijuana users and nonusers.
Performance measure Marijuana users
(n = 10, mean(SE))
Nonusers
(n = 14, mean(SE))Results (ANCOVA)
Errors of omission (Press for X) 0.20 (0.13) 0.14 (0.14) F(1,19) = 0.63(p < 0.44)
Errors of omission (Press for all except X) 0.40 (0.22) 0.29 (0.22) F(1,19) = 0.21(p < 0.65)
Errors of commission (Press for X) 1.00 (0.30) 0.57 (0.17) F(1,19) = 1.40(p < 0.25)
Errors of commission (Press for all except X)4.10 (1.00) 4.57 (1.20) F(1,19) = 1.34(p < 0.26)
Reaction time (s, Press for X) 0.40 (0.02) 0.39 (0.02) F(1,19) = 0.23(p < 0.63)
Reaction time (s, Press for all except X) 0.41 (0.01) 0.41 (0.02) F(1,19) = 0.01(p < 0.91)

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This was used as a confirmation that the task was re-
cruiting the response inhibition circuitry as anticipated.
Although the ideal contrast was “non-X” minus ‘”X”, the
power, when considering covariates, was too small to
report significant differences between conditions. How-
ever, the “Press for all letters except X” minus rest con-
trast had sufficient power to reveal the expected pattern
of activation. This is presented in Figure 1 for the non-
users and areas included the dorsolateral prefrontal cor-
tex, premotor cortex, supplementary motor cortex, cere-
bellum, insula and superior temporal gyrus.
Random effect analyses between groups were per-
formed using the “Press for all letters except X” condi-
tion minus the “Press for X” condition. A 2 sample t-test
analysis without any covariates was used to confirm that
there were differences between users and nonusers dur-
ing challenge of the response inhibition circuitry. There
were no areas that showed significantly less activation in
marijuana users than nonusers. However, marijuana us-
ers did show significantly more activation than nonusers
in typical response inhibition areas, including the pre-
central gyrus, superior, middle, orbital and inferior fron-
tal gyri, lingual gyrus and supramarginal gyrus. Again,
however, the power was insufficient when controlling for
other drug exposures. For example prenatal marijuana
has been shown to play a role in response inhibition [21],
and even though there was no significant difference be-
tween groups for prenatal drug effects, it was deemed
important to determine if in fact these exposures were
impacting the results of current marijuana use on neural
functioning.
Thus, two multiple regression analyses were per-
formed, with this contrast of “Press for all letters except
X” minus “Press for X”, using prenatal marijuana expo-
sure and prenatal nicotine exposure as covariates. The
results suggest that these did not contribute to the differ-
ences between groups. In addition, further multiple re-
gressions of the same contrast were performed with: 1)
current alcohol as a covariate; 2) current nicotine as a
covariate; 3) current alcohol and current nicotine to-
gether as covariates; and 4) current alcohol, current nico-
tine and prenatal marijuana together as covariates. The
results suggest that these other variables do contribute to
the differences between groups as the results were no
longer significant with these analyses. Therefore, to en-
sure sufficient power while still controlling for other
drug exposures, further analyses were conducted using
the “Press for all letters except for X” condition minus
the “Rest” condition. It was anticipated that this type of
analysis would allow for the identification of differences
between groups in motor, visual and other brain regions
that otherwise would not be possible to identify using the
“Press for all letters except X” condition minus the “Press
Figure 1. Non marijuana smoking group analysis rendered
at FWE = 0.05, corrected for multiple comparisons, for
clusters larger than 50 voxels. L represents the view from
the left side of the brain while R represents the view from
the right side of the brain. The left most image is a medial
sagittal view of the cerebellar and premotor/supplementary
motor activations for the “Press for all letters except X”
minus “Rest” contrast. The middle and right most image
represent lateral sagittal views of the prefrontal cortex and
parietal area activations for the same contrast.
for X” condition.
Multiple regression analyses of the new contrast yielded
a significant positive relationship between amount of
marijuana use and neural activity even with each of the
prenatal and current drug variables as covariates. In ad-
dition, the results were also significant when controlling
for acute marijuana use (by removal of those participants
that smoked marijuana on the day of testing). Only the
results from the analysis with prenatal marijuana, current
nicotine and current alcohol together as covariates are
reported below.
The most robust effect of this study was the significant
increase in neural activation in several regions as the
amount of “self reported” marijuana smoked increased.
These results were observed for the ‘Press for all letters
except X’ minus ‘Rest’ contrast, at a p value corrected
multiple comparisons for cluster level at 0.05, in a large
cluster of 2578 voxels that included the right thalamus (x
y z = 3 –18 10; Figure 2), the right premotor cortex (x y z
= 33 6 30; Figure 2) and the right middle frontal gyrus (x
y z = 33 18 60; Figure 2). Results also showed greater
activation in the inferior parietal lobe/supramarginal
gyrus (x y z = 48 –48 50) and the precuneus (x y z = 3
–66 60), uncorrected at 0.05; cluster size of 689 voxels
as marijuana use increased.
4. Discussion
This study examined BOLD fMRI response among re-
gular current marijuana users and nonusers during a
Go/No-Go task. Although differences in behavioral per-
formance were non-significant, the two groups differed
in their pattern of neural activation, with more BOLD
activity occurring in a dose dependent manner as the
quantity of marijuana use increased. Furthermore, the in-
creased activity was still significant after controlling for
other drugs such as alcohol, nicotine and prenatal mari-
juana.

A. M. SMITH ET AL.
130
Figure 2. Images representing the positive relationship be-
tween marijuana use and thalamic activation (left most
image) and the right prefrontal cortex activation (right
most image) for the “Press for All Letters except X” minus
“Rest” contrast at FWE, p = 0.05, corrected for multiple
comparisons, with only clusters with more than 200 signifi-
cantly activated voxels.
The most substantial differences in activation were
found to be right lateralized in the premotor cortex and
the middle frontal gyrus or dorsolateral prefrontal cortex.
Response inhibition in healthy controls involves a dis-
tributed network that includes these areas as well as pa-
rietal areas [3,24,25]. During response inhibition, the
premotor cortex is involved in response competition and
the preparatory process leading to correct initiation or
suppression of movement [24,26]. Given that there were
non-significant behavioral differences in errors of com-
mission between the two groups it is unlikely that this
increased activation of the premotor cortex is related to
increased motor responses in the users. Also, [4] found
that mostly left premotor cortex is involved during
preparation to respond. Thus, the findings from the pre-
sent study suggest that marijuana smokers may need to
compensate by recruiting homologous contralateral areas
in order to correctly initiate or suppress responses.
In a response inhibition study by Casey et al. [27], it
was found that the volume of activation in the middle
frontal gyrus is correlated with age, suggesting that this
structure may be important in the developmental im-
provement of inhibitory abilities. Furthermore, several
studies support that the development of the response in-
hibition circuitry continues to develop well into late ado-
lescence [28-32]. Given that the OPPS sample is com-
prised of young adults who on average have been smok-
ing marijuana for 4.5 years (i.e. started smoking during
adolescence), it is possible that their exposure to mari-
juana over those years may have compromised middle
frontal gyrus development and thus, explain the dose
dependent increase in activation. These results empha-
size the importance of early education about the potential
cognitive impact of early exposure to marijuana.
Another brain region that showed a significant positive
relationship between activation and amount of marijuana
smoked was the right thalamus. The thalamus is included
in various circuits including the frontal-striatal-thalamic
and the cingulo-opercular networks [2]. The frontal-stria-
tal-thalamic circuit has been found to support the devel-
opment of inhibitory control and correlate with better
performance on inhibitory tasks [30,31]. The cingulo-
opercular network, which also includes the thalamus,
among other brain regions, is thought to support response
rate. Response rate refers to the ability to apply cognitive
skills in a consistent and flexible manner depending on a
task’s demands [2]. A voxel-based morphometry study
revealed that marijuana users had increased gray matter
density in the right precentral gyrus and right thalamus
compared to nonusers [33]. The authors speculated that
changes in one component of the brain, gray matter in this
case, may be compensated for by changes in a neighbour-
ing component, for example, gray matter displacement
caused by a decrease of nearby white matter. Therefore,
it is possible that the greater activation found in the right
thalamus of marijuana users in the present study may be
related to a change in white matter in other areas forming
part of the inhibitory pathways. Consequently, compen-
sation on such circuits may have occurred by increased
activation of the right thalamus in order for users to keep
up with the behavioral demands of the “Press for all ex-
cept X” condition. The inability to assess white and gray
matter volumes in the present study is a limitation and
future research is required.
The present study also revealed a trend for a positive
relationship between amount of marijuana smoked and
greater bilateral activation in the inferior parietal lobe.
Several imaging studies have also found increased parie-
tal lobe activation in marijuana users [1,6,9]. More spe-
cifically, right parietal regions have been implicated in
sustained attention [34], with neuroimaging studies re-
porting parietal activation during attentionally demand-
ing tasks to be in the superior, rather than inferior, parie-
tal lobule [3]. Together, these results suggest that mari-
juana users may recruit additional parietal regions in
order to properly sustain their attention during response
inhibition tasks.
Additionally, the parietal lobes are also part of the
frontal-parietal circuit, which has also been associated
with inhibitory control and working memory [35]. This
network has been found to continue to reorganize
through adolescence, becoming more distinct and segre-
gated from one another and further integrating long dis-
tance connections [36]. As previously mentioned, the
OPPS sample of users had been smoking marijuana since
adolescence. Therefore, it is possible that the relationship
between neural activation and marijuana consumption
may be due to either a delay or to an altered development
of such a network due to marijuana exposure during
years where response inhibition circuits are still under

A. M. SMITH ET AL.
131
development.
Another positively related trend from the present study
was observed in the precuneus. This region is thought to
have a role in error awareness and monitoring [24,37],
which is an important aspect of prefrontal cortex func-
tion since error detection allows for the correction and
improvement of task performance [38]. Support for this
evidence is found in electrophysiological and lesion
studies that propose that this region may be involved in
evaluative functions such as monitoring behaviour [37,
39]. Moreover, Nagahama et al. [40] have found that the
precuneus is activated when external feedback shifts
from “correct” to “incorrect” during tasks where subjects
are required to alter stimulus-response judgments. Al-
though during the Go/No-Go task, in the present study
did not introduce an error awareness task, these findings
may indicate that marijuana users need to work harder in
order to monitor their response and be aware of errors
during their performance. However, further testing with a
response inhibition task that includes error awareness
recognition should be carried out in order to confirm this
result.
Although other fMRI studies researching response in-
hibition in marijuana users have found greater activation
in prefrontal regions including the dorsolateral prefrontal
cortex [1,6], the present study provides further evidence
that other brain regions of response inhibition circuitry
may also be altered. Other frontal areas and regions of
the parietal lobe, as well as subcortical areas are also
affected by marijuana exposure. Moreover, the three cir-
cuits involved in response inhibition, namely the fron-
tal-striatal thalamic circuit, the cingulo-opercular circuit
and frontal-parietal circuit, are all still under develop-
ment during adolescence [27,30-32,41,42]. Therefore,
the overactive brain regions observed in this investiga-
tion may be due not only to the current marijuana use but
also to the relatively long term marijuana exposure dur-
ing those crucial years in adolescence.
The strength of this study was the ability to control for
an unparalleled number of lifestyle variables including
IQ, current nicotine and alcohol use and prenatal mari-
juana, nicotine, and alcohol exposure. The well controlled
sample strengthens the validity of the results and pro-
vides outcomes that are able to shed light on more exclu-
sive contributions of marijuana on the response inhibi-
tion network than previous studies.
The limitations of the study include that the sample
was small and a primarily Caucasian, middle-class po-
pulation. Thus, these results cannot be generalized to
other ethnic or socioeconomic status populations. How-
ever, this is a low risk population and these effects are
significant, suggesting that a high risk population would
be even more likely to show a negative impact of mari-
juana use given the other risk factors.
The present study also used a block design rather than
an event related design. A block design does not permit
the separation of Go from the No-Go components of the
brain activity. Thus, an event-related study may have
helped to separate response inhibition from other cogni-
tive processes. It is also difficult to ever truly remove a
drug’s effect from a BOLD study and thus it must be
considered that current use of nicotine impacted the re-
sults even after using it as a covariate. Similarly, there
was no measure of alcohol consumption on the day of
testing other than the self-report of each participant. Al-
though the self-report and urine sample values were
highly correlated for those drugs tested in the urine, this
was an oversight for the alcohol consumption and should
be rectified with the addition of a breath alcohol level
assessment in future research. Finally, there was no ab-
stinence period for the participants of either group. How-
ever, careful statistical analyses were performed including
and not including those participants who smoked mari-
juana on the day of testing. Even though these analyses
had less power than the reported results, the same posi-
tive relationship between amount of marijuana smoked
and neural activity was observed. This suggests that the
reported results are indicative of the regular marijuana
use and not only acute marijuana effects. Future research
will test participants who have stopped using marijuana
for at least 6 months.
In conclusion, adolescent use of marijuana can have
detrimental effects on the brain that can be observed in
young adulthood. The findings in this study suggest that
increase in neural activation with increased marijuana
use may be due to a form of neural compensation or an
altered neural development, or both. Also, this may occur
not only in the prefrontal cortex but also in the extensive
neural network required for inhibitory control, a cogni-
tive process important for executive functioning and thus
success in establishing and reaching appropriate goals
during adulthood.
5. Acknowledgments
The research was funded through an Ontario Research
and Development Challenge Fund grant. The authors
would like to acknowledge the contributions of Robert
Gray and the MRI technologists at the Ottawa Hospital.
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