The negative health effects of THC(Tetrahydrocannabinol) on memory function of human brain
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Effectsof9-TetrahydrocannabinolTHConHumanExecutiveFunction.pdf
Default Mode Network in the Effects of D9- Tetrahydrocannabinol (THC) on Human Executive Function Matthijs G. Bossong1,2*, J. Martijn Jansma1, Hendrika H. van Hell1, Gerry Jager1,3, René S. Kahn4,
Nick F. Ramsey1
1 Rudolf Magnus Institute of Neuroscience, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands, 2 Institute of
Psychiatry, Department of Psychosis Studies, King’s College London, London, United Kingdom, 3 Division of Human Nutrition, Wageningen University, Wageningen, The
Netherlands, 4 Rudolf Magnus Institute of Neuroscience, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands
Abstract
Evidence is increasing for involvement of the endocannabinoid system in cognitive functions including attention and executive function, as well as in psychiatric disorders characterized by cognitive deficits, such as schizophrenia. Executive function appears to be associated with both modulation of active networks and inhibition of activity in the default mode network. In the present study, we examined the role of the endocannabinoid system in executive function, focusing on both the associated brain network and the default mode network. A pharmacological functional magnetic resonance imaging (fMRI) study was conducted with a placebo-controlled, cross-over design, investigating effects of the endocannabinoid agonist D9-tetrahydrocannabinol (THC) on executive function in 20 healthy volunteers, using a continuous performance task with identical pairs. Task performance was impaired after THC administration, reflected in both an increase in false alarms and a reduction in detected targets. This was associated with reduced deactivation in a set of brain regions linked to the default mode network, including posterior cingulate cortex and angular gyrus. Less deactivation was significantly correlated with lower performance after THC. Regions that were activated by the continuous performance task, notably bilateral prefrontal and parietal cortex, did not show effects of THC. These findings suggest an important role for the endocannabinoid system in both default mode modulation and executive function. This may be relevant for psychiatric disorders associated with executive function deficits, such as schizophrenia and ADHD.
Citation: Bossong MG, Jansma JM, van Hell HH, Jager G, Kahn RS, et al. (2013) Default Mode Network in the Effects of D9-Tetrahydrocannabinol (THC) on Human Executive Function. PLoS ONE 8(7): e70074. doi:10.1371/journal.pone.0070074
Editor: Carles Soriano-Mas, Bellvitge Biomedical Research Institute-IDIBELL, Spain
Received January 27, 2013; Accepted June 16, 2013; Published July 31, 2013
Copyright: � 2013 Bossong et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study is performed within the framework of Top Institute Pharma (www.tipharma.com), project number T5-107. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: Storz & Bickel supplied the Volcano vaporizer used in this study. There are no further patents, products in development or marketed products to declare. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.
* E-mail: [email protected]
Introduction
The endocannabinoid (eCB) system is a retrograde messenger
system that regulates both excitatory and inhibitory neurotrans-
mission, and consists of cannabinoid receptors and accompanying
endogenous ligands [1]. Recently, the eCB system has emerged as
a potential candidate for pharmacological targeting of psychiatric
syndromes including addiction [2] and schizophrenia [3]. Impor-
tantly, the eCB system has been associated with executive
functions which are also affected in various psychiatric disorders.
Modulation of the eCB system by administration of cannabis or
D9-tetrahydrocannabinol (THC), the main psychoactive compo- nent in cannabis and partial agonist of the cannabinoid CB1
receptor, impairs performance on various executive function
paradigms that target high-level cognitive functions essential for
goal-directed behavior [4–8].
Goal-oriented behavior has recently been associated with
reduced neural activity in the default mode network (DMN),
which mainly consists of the posterior cingulate cortex, medial
prefrontal cortex, and bilateral inferior parietal lobules (including
the angular gyrus) [9–11]. Failure to reduce DMN activity impairs
performance on various cognitive tasks [12–15]. Moreover,
psychiatric patients such as patients with schizophrenia or
attention-deficit hyperactivity disorder (ADHD), who exhibit a
strong decline in cognitive function, display an inability to
deactivate the DMN during performance of executive function
paradigms [11,16–23]. Collectively, this suggests a role for the
DMN in cognitive function deficits.
The aim of the present study was to elucidate the role of the
eCB system in executive function, in terms of performance and
brain activity in both the DMN and the task-related network. To
this end, a pharmacological functional MRI (fMRI) study was
performed with acute THC administration, using a placebo-
controlled cross-over design and a continuous performance task
paradigm with identical pairs (CPT-IP) in healthy subjects [24,25].
This version of the CPT is characterized by a heavy reliance on
executive function, as it requires fast and continuous updating of
information while short-term memory load is relatively small [26].
Previous imaging studies using CPT-IP paradigms have shown
activation of an executive system predominantly consisting of
frontal and parietal regions [24,25]. We compared performance
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on the CPT-IP task after placebo and after THC administration,
and assessed the role of the DMN and the executive system in the
effect of THC. On the basis of neuropsychological findings [4–7],
it was expected that THC administration would reduce perfor-
mance on the CPT-IP task. This was hypothesized to be associated
with reduced deactivation of the DMN, as this has been shown to
impair cognitive performance [12–15]. In addition, an increase in
perceived erroneous responses may result in increased effort to
maintain good performance levels, most likely reflected as elevated
activity in the executive system [27,28].
Materials and Methods
This study is part of the Pharmacological Imaging of the
Cannabinoid System (PhICS) project, the design and objectives of
which are provided in a methodological paper [29].
Ethics Statement The study was approved by the Independent Ethics Committee
of the University Medical Center Utrecht, the Netherlands, in
accordance to the Declaration of Helsinki 2008. All volunteers
gave written informed consent before entry into the study.
Subjects Twenty-three healthy male right-handed subjects were recruited
through flyers, posters and internet advertisements. All subjects
were incidental cannabis users, defined as having used cannabis at
least four times but at most once a week in the year before
inclusion in the study. All subjects were in good physical health as
assessed by medical history and physical examination, and were
screened for axis I psychiatric disorders using the Mini Interna-
tional Neuropsychiatric Interview for DSM-IV clinical disorders.
Subjects were asked to refrain from cannabis for at least two weeks
before the first study day until study completion. Illicit drug use
other than cannabis was not within six months prior to inclusion.
Compliance was tested by means of a urine sample at the
beginning of each test day. Subjects needed to abstain from
alcohol for 48 hours before each study day, and caffeine intake and
smoking were not allowed from the moment of arrival until the
end of a study day. For further details on inclusion and exclusion
criteria we refer to Van Hell et al. [29].
Results are reported on twenty out of the twenty-three included
subjects. One subject did not complete the study procedure due to
high blood pressure levels. Two other subjects were excluded
because of an absence of detectable THC plasma levels and
technical malfunction during scanning, respectively. Subject
characteristics are summarized in Table 1.
Design and Procedure Using a double-blind, randomised design, subjects underwent
two scanning sessions: one with placebo administration and one
with THC, balanced over subjects. As not all subjects could be
included, eight of the twenty subjects received placebo first. Study
days were scheduled two weeks apart to allow for complete
clearance of drugs. On study days, subjects performed three
cognitive paradigms, during which fMRI scans were obtained.
One of these paradigms was the CPT-IP. Paradigm sequence was
balanced over subjects, but remained unchanged within subjects
across sessions. Results of other assessments are reported elsewhere
[29–33]. Although there is some overlap in subjects participating
in our current and previous studies, none of the published studies
have identical experimental groups.
Subjects received subsequent doses of THC or placebo with 30
minutes intervals. Drugs were administered 7 minutes before the
start of each fMRI task using a Volcano H vaporizer (Storz-Bickel GmbH, Tuttlingen, Germany) [34,35]. The first THC dose was
6 mg, followed by three doses of 1 mg each to maintain stable
levels of CNS effects. See Van Hell et al. [29] for detailed study
procedures.
Drug Levels and Behavioral Measurements Venous blood samples were collected 5 and 27 minutes after
administration to determine plasma concentrations of THC and
its two most important metabolites, 11-hydroxy-THC and 11-nor-
9-carboxy-THC, and were processed according to Zuurman et al.
[35].
Subjective effects were determined with two sets of visual
analogue scales (VAS) [36,37]. The first rating scale consisted of
16 VAS from which three factors were calculated, corresponding
to alertness, contentedness, and calmness [36]. From a second set
of 13 VAS [37], composite scores of ‘external perception’ and
‘internal perception’ were calculated, whereas ‘feeling high’ was
analyzed individually, as validated by Zuurman and colleagues
[31]. Computerized versions of both rating scales were performed
consecutively at baseline and before and after task performance.
VAS data were corrected for baseline values, and each set of VAS
was analyzed with a multivariate approach to repeated measures
ANOVA with factors drug (2 levels: placebo and THC), time (2
levels: before and after task performance) and scale (3 levels for
each set of VAS). Post hoc repeated measures ANOVA was
performed to further investigate effects on individual VAS items.
Heart rate was measured regularly at fixed intervals before
scanning, and monitored continuously during scanning. Mean
heart rate during scanning was calculated by dividing the total
number of heart beat trigger signals by the duration of the CPT-IP
task [38]. Mean heart rate during scanning was corrected for mean
baseline values, and placebo and THC sessions were statistically
compared with a paired t test.
Table 1. Subject characteristics (n = 20).
Characteristic Mean ± SD Range
Age (years) 22.964.9 18–40
IQ 105.665.6 97–114
Height (cm) 185.967.9 175–201
Weight (kg) 77.0611.3 60–110
BMI (kg/m2) 22.262.1 18.5–27.2
Cannabis use last year (Occasions) 22.5615.2 4–52
Cannabis use lifetime (Occasions) 3376448 32–1415
Age of cannabis use onset 15.761.7 13–21
Years of cannabis use 7.365.1 1–25
Tobacco smoking (Cigarettes/week) 57.6660.8 0–140
Alcohol consumption (Units/week) 12.567.8 2–30
Coffee consumption (Units/week) 17.4612.4 0–40
Illicit drug use (Occasions lifetime) 2.064.0 0–17
Use of tobacco, alcohol and coffee was given for the year before inclusion in the study. Subjects refrained from cannabis for at least two weeks before the first study day until study completion and from alcohol for 48 hours before each study day. Caffeine intake and smoking were not allowed from the moment of arrival until the end of a study day. Illicit drug use other than cannabis was at least more than six months before the first study day. All subjects showed negative urine screening at both study days. doi:10.1371/journal.pone.0070074.t001
THC, Default Mode Network and Executive Function
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Task Paradigm Executive function was assessed with a CPT with identical pairs
(CPT-IP) consisting of two different task conditions (Figure 1)
[24,25]. In the experimental condition (CPT-IP), participants were
presented with a series of four-digit numbers, and were instructed
to press a button as quickly as possible when two consecutive
numbers were identical. In a control task (CT), subjects were
always presented with the same stimulus (‘1234’), and were
instructed to watch the stimuli, but not to respond. This task was
designed to control for the simple visual components of watching
flashing numbers.
The CPT-IP and CT tasks were given in alternating blocks of
30 s each. Six blocks of each task were presented, together with six
rest blocks. The order of blocks was counterbalanced within
sessions. A total of 40 numbers per block was presented. Every
number appeared for 700 ms, followed by a fixation cross of
50 ms. For the CPT-IP task only, the number of targets per block
varied from seven to nine, with an average of eight targets per
block. In addition, each CPT-IP block contained eight distracters,
defined as numbers consisting of similar digits as the preceding
number, but presented in another order. Total task duration was
11 minutes. Numbers differed for both study days for all subjects.
Outcome measures for the CPT-IP task included reaction time
for hits (RT), the mean percentage of correctly identified targets
(% hits), and the mean percentage of incorrectly identified targets
(% false alarms). Group differences in RT and performance
accuracy between placebo and THC were analyzed with paired t
tests.
Image Acquisition Image acquisition was performed on a Philips Achieva 3.0 Tesla
scanner (Philips Medical Systems, Best, the Netherlands). Func-
tional images were obtained using a 3D PRESTO-SENSE pulse
sequence [39] (parameters: scan time 0.6075 s; TR 22.5 ms (in
contrast to EPI, for PRESTO the TR is much shorter than the
time to scan one volume, see [39]; TE 33.2 ms; flip angle = 10u; FOV 22462566160; matrix 56664640; voxel size 4 mm isotropic; 40 slices (sagittal orientation); 1105 volumes). A high-
contrast volume with a flip angle 27u was scanned for registration
purposes. A T1-weighted structural image was obtained for
anatomical registration (parameters: TR 9.5 ms; TE 4.7 ms; flip
angle = 8u; FOV 220.862406159.6; matrix 36864006266; voxel size 0.6 mm isotropic, 266 slices (sagittal orientation)).
Functional MRI Analysis Functional MRI data were preprocessed and analyzed using
SPM5 (Wellcome Trust Centre for Neuroimaging, London, UK).
Preprocessing included realignment of functional images, co-
registration with the anatomical volume using the flip angle of 27u volume, spatial normalization into standard MNI space, and
smoothing (FWHM = 8 mm), as described previously [30,31,33].
There were no significant differences between sessions in scan
quality in terms of the average standard deviation of time series.
First level single subject analysis included a general linear model
regression analysis using a factor matrix with factors for the CPT-
IP and CT condition, as well as the instructions that were
presented during the task and factors to correct for slow drifts in
the signal up to 0.004 Hz. Group activity maps were created for
both the placebo and THC session for the CPT-IP minus CT
contrast.
We chose to perform ROI analyses, because we expected the
default mode and executive system to act as connected networks.
In addition, this analysis (unlike voxel-wise whole brain analysis)
allows for both calculation and presentation of effect sizes and
follow-up analysis, and has sufficient power for smaller samples
[40–42]. We preselected ‘task’ voxels that showed significant signal
changes associated with the experimental paradigm (thresholded
at |t| .4.6, p,0.0001). To prevent session bias in voxel selection, voxels were included if they exceeded threshold in either the
placebo or THC session. Regions of interest (ROIs) were identified
by clustering groups of at least ten neighboring active voxels
(640 mm3). We chose a lenient threshold for voxels selection to
ensure that we included most regions showing signal changes
related to the task. Notably, the threshold for ROI identification
has no direct relationship to the tested experimental hypotheses
[40].
ROIs were divided in two groups: ROIs showing task-related
increases are referred to as task-induced activation (‘TIA’) ROIs.
ROIs based on voxels showing signal decrease are referred to as
task-induced deactivation (‘TID’) ROIs. Mean signal change for
each ROI, each subject, and each session (placebo and THC) was
based on regression coefficients (b values) for the CPT-IP
condition averaged over voxels in each ROI, extracted using the
Marsbar SPM tool [43].
It is at this stage that statistical hypothesis testing was conducted,
using SPSS 17. To measure THC effects on brain activity in TIA
ROIs, a multivariate approach to repeated measures ANOVA was
used with drug (2 levels: placebo and THC) and ROI (15 levels: all
TIA regions included) as within-subject factors. To measure THC
effects on brain activity in TID ROIs, a multivariate approach to
repeated measures ANOVA was used with drug (2 levels) and ROI
(4 levels: TID regions) as within-subject factors (see the results
section for details about ROIs).
To directly compare effects of THC in TIA and TID networks,
mean regression coefficients for the CPT-IP condition were
averaged over all included voxels for either network, for both the
placebo and THC condition. To measure THC effects on network
activity, a multivariate approach to repeated measures ANOVA
was used with drug (2 levels: placebo and THC) and network (2
levels: TIA and TID network) as within-subject factors.
Figure 1. Schematic outline of the task used to assess executive function. The task consists of a control (CT, left) and an experimental condition (CPT-IP, right), during which four-digit numbers are presented in sequence. In the experimental condition, subjects have to press a button as quickly as possible when two consecutive numbers are identical. No response is required for the control condition. See for detailed information the Materials and methods section. doi:10.1371/journal.pone.0070074.g001
THC, Default Mode Network and Executive Function
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Correlation Analyses For further understanding of the acute effects of THC on
executive function, correlation analyses were performed between
task performance (percentage of correct responses, which reflects
both hits and false alarms), brain activity (TID and TIA network),
peak plasma concentrations of THC and its two main metabolites,
and subjective effects after THC administration (Pearson’s r).
Follow up analyses are presented as a further descriptive
exploration of the main hypothesis test, and are, as such, not
corrected for multiple comparisons.
Results
Drug Levels and Behavioral Measurements Plasma concentrations of THC and its main metabolites were
78.4627.0 ng/ml (THC), 3.964.6 ng/ml (11-nor-9-carboxy- THC) and 2.562.0 ng/ml (11-hydroxy-THC), 5 min after inhalation of 6 mg THC.
Overall repeated measures ANOVA analysis of the VAS scales
‘feeling high’, ‘internal perception’ and ‘external perception’ [37]
revealed a significant difference in the drug by time interaction
between VAS scales (drug * time * VAS scale interaction,
F(2,18) = 7.90, p = 0.003), a significant difference in the effect of
THC both between VAS scales (drug * VAS scale interaction,
F(2,18) = 11.44, p = 0.001) and between time points (drug * time
interaction, F(1,19) = 8.37, p = 0.009), a significant increase in
VAS score with THC administration (drug effect, F(1,19) = 15.06,
p = 0.001), and a higher VAS score after task performance (time
effect, F(1,19) = 16.01, p = 0.001). Post hoc analysis of the VAS
scale ‘feeling high’ showed a significant increase in VAS score with
THC administration (drug effect, F(1,19) = 19.10, p,0.001) and a higher VAS score after task performance (time effect,
F(1,19) = 7.39, p = 0.014), without differences in the effects of
THC between time points (drug * time interaction, F(1,19 = 3.64,
p = 0.072). Analysis of ‘external perception’ (reflecting mispercep-
tion of external stimuli or changes in the awareness of the
environment) showed a significant increase in VAS score with
THC administration (drug effect, F(1,19) = 11.03, p = 0.004) and a
higher VAS score after task performance (time effect,
F(1,19) = 8.11, p = 0.010), with a significant difference in the effect
of THC between time points (drug * time interaction,
F(1,19 = 13.09, p = 0.002). Post hoc analysis of the VAS scale
‘internal perception’ (reflecting inner feelings that do not
correspond with reality) did not show any significant effects (see
Table 2).
Overall repeated measures ANOVA analysis of the VAS scales
‘alertness’, ‘contentedness’, and ‘calmness’ [36] revealed a
significant increase in VAS score with THC administration (drug
effect, F(1,19) = 12.24; p = 0.002), without any significant differ-
ences in VAS scores between time points or in the effects of THC
between VAS scales or time points (time effect, F(1,19) = 1.66,
p = 0.213; drug * VAS scale interaction, F(2,18) = 2.24, p = 0.135;
drug * time interaction, F(1,19) = 1.67, p = 0.212; drug * time *
VAS scale interaction, F(2,18) = 1.16; p = 0.337). Post hoc analysis
of individual VAS scales showed a significant decrease in VAS
score with THC administration for ‘alertness’ (drug effect,
F(1,19) = 9.24, p = 0.007), ‘contentedness’ (drug effect,
F(1,19) = 10.03, p = 0.005), and ‘calmness’ (drug effect,
F(1,19) = 10.10, p = 0.005). The VAS score on ‘alertness’ was
significantly lower after task performance (time effect,
F(1,19) = 15.58, p = 0.001). Results of VAS post hoc analyses are
summarized in Table 2.
Heart rate increased significantly after THC compared with
placebo (22.2614.5 and 21.567.8 bpm increase compared to baseline (6 SD), respectively; t(17) = 26.85, p,0.001). For a more detailed description of drug levels and behavioral measurements
following THC see Van Hell et al. [29].
Task Performance THC administration significantly decreased the percentage of
correctly identified targets (from 83.762.9% to 74.764.3%, t(19) = 2.66, p = 0.016) and enhanced the percentage of false
alarms (from 3.560.7% to 5.760.9%, t(19) = 23.76, p = 0.001). Reaction times on the CPT-IP did not differ between placebo and
Table 2. Subjective effects of D9-tetrahydrocannabinol (THC) (n = 20).
Repeated measures ANOVA effects (F(1,19))
VAS Assessment Drug Time Drug* Time Mean Placebo score (± SD) Mean THC score (± SD)
Feeling High 19.10, p,0.001* 7.39, p = 0.014* 3.64, p = 0.072 2.6366.41 27.00625.99
Internal Perception 3.42, p = 0.080 2.36, p = 0.142 2.28, p = 0.148 0.1560.63 3.1567.06
External Perception 11.03, p = 0.004* 8.11, p = 0.010* 13.09, p = 0.002* 0.9862.24 9.15610.29
Alertness 9.24, p = 0.007* 15.58, p = 0.001* 0.10, p = 0.756 27.4467.68 217.03612.72
Contentedness 10.03, p = 0.005* 1.13, p = 0.300 0.49, p = 0.491 23.6068.05 211.6869.73
Calmness 10.10, p = 0.005* 0.071, p = 0.793 3.17, p = 0.091 4.94612.82 29.63618.20
Statistical analysis was performed with baseline corrected values using a multivariate approach to repeated measures ANOVA with drug and time as factors. *Significant difference between placebo and THC (p,0.05). VAS, Visual Analogue Scale. doi:10.1371/journal.pone.0070074.t002
Figure 2. Task performance. The figure shows (left) the mean percentage of correctly identified targets, (middle) the mean percent- age of false alarms, and (right) reaction times of correct responses after placebo and THC administration (n = 20; mean 6 SEM). * Significant difference between THC and placebo (p,0.05). doi:10.1371/journal.pone.0070074.g002
THC, Default Mode Network and Executive Function
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THC sessions (538.567.1 and 552.0611.0 ms, respectively; t(19) = 21.07, p = 0.296) (all 6 SEM, see Figure 2).
Selection of Regions of Interest Task activity was measured in a set of regions showing task-
induced deactivation (TID) and a set of regions showing task-
induced activation (TIA). TID showed a network of four regions,
comprising posterior cingulate cortex, left inferior temporal gyrus,
right cerebellum and left angular gyrus (Figure 3A). TIA yielded a
network of 15 brain regions, comprising bilateral prefrontal cortex,
parietal cortex, precentral gyrus, visual cortex, and thalamus, as
well as anterior cingulate cortex, mid cingulate gyrus, vermis, and
right middle temporal cortex (Figure 3B).
Effects of THC on Task-induced Deactivation Activity in TID regions was significantly increased after THC
administration (F(1,19) = 13.20; p = 0.002) (Figure 4, right). There
was no significant drug * ROI interaction in TID ROIs
(F(1,19) = 0.06, p = 0.98) (Figure 3A, Figure S1A, Figure S2).
Effects of THC on Task-induced Activation Brain activity in TIA regions was not affected by THC
administration (F(1,19) = 0.02; p = 0.90), indicating that THC
did not induce a change in the pattern of TIA activity during
CPT-IP (Figure 4, left). There was no significant difference in the
effect of THC between TIA ROIs (drug * ROI interaction,
F(1,19) = 0.72, p = 0.71) (Figure 3B, Figure S1B, Figure S2).
Figure 3. Effects of THC administration on activity in regions of interest (ROIs). The figure shows A, task-induced deactivation (TID), and B, task-induced activation (TIA). ROIs are defined in CPT-IP minus CT group activity maps, pooled over placebo and THC (n = 20; t.|4.6|, p,0.0001 uncorrected, clusters $10 voxels). L, left; R, right. doi:10.1371/journal.pone.0070074.g003
THC, Default Mode Network and Executive Function
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Task-induced Activation vs Task-induced Deactivation A direct comparison of THC effects on the TIA and TID
networks, using the average activity in each network, revealed a
significant interaction effect between drug and network
(F(1,19) = 6.97; p = 0.02), reflecting that the TID network was
more sensitive to the effects of THC than the TIA network
(Figure 4, Figure 5).
Correlations Task performance (percentage of correct responses, see Figure
S3) showed a significant negative correlation with activity in the
TID network after THC (r = 20.43, p = 0.03) (Figure 6). Follow up analysis in the four TID ROIs indicated a significant negative
correlation in posterior cingulate cortex (r = 20.38, p = 0.049), right cerebellum (r = 20.44, p = 0.026) and left angular gyrus (r = 20.53, p = 0.008). No significant correlation was found between performance and TIA activity (r = 20.03; p = 0.91) (Figure 6).
Peak THC plasma concentration showed a significant negative
correlation with activity in the TID network after THC
(r = 20.55, p = 0.007). Follow up ROI analysis in the four TID ROIs indicated a significant negative correlation in posterior
cingulate cortex (r = 20.43, p = 0.033), right cerebellum (r = 20.42, p = 0.038) and left angular gyrus (r = 20.58, p = 0.004). No significant correlation was found between peak
THC plasma concentration and TIA activity (r = 20.32; p = 0.186). Levels of 11-OH-THC and 11-nor-9-carboxy-THC
did not show significant correlations with either VAS subjective
ratings, task performance or network activity. Subjective effects did
not show significant correlations with other measures of interest.
Discussion
The role of the eCB system in executive function was studied in
an fMRI study with a THC challenge, focusing on processing of
continuously updated information and the role of DMN. After
THC administration, subjects showed impaired task performance,
reflected in both an increase in false alarms and a reduction in
detected targets. Brain regions that were deactivated during the
task showed less deactivation after THC than after placebo. In
addition, after THC, task performance was negatively correlated
with activity in the deactivated regions. In contrast, regions that
were positively activated by the task did not show THC-induced
changes in activity. Together, these results indicate that the DMN
plays a role in the effects of THC on executive function. Effects of
THC on DMN activity were predominantly found in the posterior
Figure 4. Brain activity in the TIA (left) and TID network (right, all voxels combined). The figure shows activity after administration of placebo (white) and THC (black) (n = 20; mean 6 SEM). A significant interaction effect between drug and network indicates that THC had a different effect on activity in the TID than in the TIA network. * Significant effect (p,0.05). TIA, task-induced activation; TID, task- induced deactivation; a.u., arbitrary units. doi:10.1371/journal.pone.0070074.g004
Figure 5. Activity over time in the TID (upper graph) and TIA network (lower graph) during CPT-IP performance. The figure shows activity after placebo (blue) and THC (green) administration (n = 20; mean). The upper graph demonstrates the consistently smaller deactivation in the TID network after THC administration, while the lower graph shows that activity in TIA ROIs is virtually unchanged after THC. TIA, task-induced activation; TID, task-induced deactivation; au, arbitrary units. doi:10.1371/journal.pone.0070074.g005
THC, Default Mode Network and Executive Function
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cingulate cortex and angular gyrus, both considered pivotal DMN
nodes [9–11].
A possible role of DMN in executive function is addressed by
the default mode interference hypothesis which states that
functions performed by the DMN interfere with successful goal-
oriented performance [44]. In the context of a normally
functioning brain, the DMN component is attenuated during
goal-directed action, and the level of attenuation is independent of
task content. Neuroimaging studies support this notion, as several
studies have linked reduced DMN activity with successful task
execution [12–15]. In addition, the level of reduction in DMN
activity appears to reflect the relative resources that need to be
allocated to task execution [45,46]. How exactly interference
occurs is largely unknown, but a possibility is that DMN functions
use similar resources as those needed for goal-oriented behavior.
Possible functions of the DMN include conscious processes that
occur in the absence of goal-oriented behavior, such as self-
referential mental processing [47], mind-wandering [48], and
mental explorations and simulations [10].
To our best knowledge, this is the first study that shows effects of
THC administration on task-induced deactivations. In line with
our finding of a lower degree of deactivation in the posterior
cingulate cortex after THC administration, a recent resting state
fMRI study showed that THC decreased functional connectivity
between the posterior cingulate cortex and a network of brain
regions collectively referred to as the left dorsal visual stream,
which is thought to be involved in attentional processes [49].
In the present study, subjective ratings of ‘feeling high’ and
‘external perception’ were increased, whereas those of ‘alertness’,
‘contentedness’ and ‘calmness’ were reduced after THC admin-
istration. Similar behavioral effects of THC on feeling high,
external perception and alertness have previously been described
[35]. Decreases in contentedness and calmness may be related to
THC administration in an MRI environment, as they were not
shown in a previous study using Positron Emission Tomography
(PET) [34].
THC-induced effects on DMN activity suggest eCB involve-
ment in regulation of default mode activity. A potential
neurobiological explanation may be found in the ‘on-demand’
modulating role of the eCB system in neurotransmitter release.
This eCB-mediated regulation of synaptic transmission is a
widespread phenomenon in the brain, and is thought to play an
important role in higher cognitive functions [1]. THC adminis-
tration may disrupt this function of the eCB system [50]. Recently,
it has been shown that negative BOLD responses are tightly
coupled to reductions in neuronal activity [51], most likely
mediated by increased GABA transmission in the DMN [52].
Importantly, increasing cognitive load was associated with more
DMN deactivation and higher GABA concentrations [52]. This
suggests that THC administration may affect DMN activity
through disruption of eCB-mediated GABA neurotransmission.
Our results may have implications for understanding impair-
ments in executive function related to psychiatric and neurological
disorders. For example, several studies have shown that schizo-
phrenia patients exhibit an inability to deactivate the DMN during
various cognitive tasks [16,17]. Impairment in capacity to reduce
DMN activity has also been identified in other patient groups [20–
23], such as youth with ADHD [18,19] and patients with
Figure 6. Correlations between performance (percentage correct responses) and brain activity. The figure shows correlations in A, TID network, B, TIA network, C, posterior cingulate cortex, D, right cerebellum, and E, right angular gyrus. TIA, task-induced activation; TID, task-induced deactivation; a.u., arbitrary units. doi:10.1371/journal.pone.0070074.g006
THC, Default Mode Network and Executive Function
PLOS ONE | www.plosone.org 7 July 2013 | Volume 8 | Issue 7 | e70074
Alzheimer’s disease [53]. Our results suggest that the eCB system
may be a factor in the abnormal DMN activity associated with
aforementioned disorders, and, as such, could be involved in
cognitive deficits in these disorders.
The current study demonstrated an extensive set of regions that
was positively activated by the task. Previous imaging studies using
executive function paradigms have shown activation of a similar
network [24,25], also referred to as the Central Executive System
(CES) [54], which has been associated with several functions
necessary for successful executive function, such as the detection
and selection of sensory stimuli [55], the subsequent linking of
stimuli to appropriate motor responses [56], and the ability to
detect erroneous responses [27,28]. Consistent with this latter CES
function, THC-induced impairment of task performance as shown
in the present study is expected to result in increased effort to
maintain good performance levels, most likely reflected as elevated
CES activity. However, THC did not affect CES activity during
performance of the task. This suggests that under influence of
THC, subjects may have been unaware of their impaired
performance, thereby dismissing the need for elevated CES
activity. Consistently, chronic cannabis users, who showed good
task performance, demonstrated diminished capacity for error
monitoring which was associated with reduced CES activity [57].
Previous studies have reported reduced activity in the CES in
psychiatric disorders such as ADHD [58,59] and schizophrenia
[60,61], an effect that is likely related to impaired task
performance [61]. One explanation for the apparent discrepancy
with the current findings could be that performance deficits as
shown in our study after THC are moderate compared to those of
psychiatric patients. For example, decreased CES activity in
schizophrenia patients in the study of Salgado-Pineda et al. [60]
was associated with a 33% reduction in the mean percentage of
correctly identified targets. This view is further supported by
studies in which CES activity of schizophrenia patients was not
reduced during adequate performance of moderately difficult
central executive tasks [62,63].
DMN-related brain activity has been shown to be affected by
human genetic variation, such as functional polymorphisms in the
catechol-O-methyltransferase (COMT) gene. Higher COMT
activity, resulting in reduced prefrontal dopamine neurotransmis-
sion, has been associated with significantly greater deactivation of
the posterior cingulate cortex of healthy volunteers [64] and
reduced deactivation in the medial prefrontal cortex of both
healthy subjects and schizophrenia patients [65] during perfor-
mance of executive function tasks. A recent multimodal neuroim-
aging study demonstrated that genetic variation in the dopamine
D2 receptor (DRD2) gene modulates connectivity strength within
the DMN during a working memory task, which was associated
with striatal dopamine transporter availability as measured with
Single Photon Emission Computed Tomography (SPECT) [66].
Interestingly, individuals with increased COMT activity appear to
have stronger responses to THC administration in terms of acute
psychotic effects and cognitive impairments [67]. Altogether, these
findings suggest the possibility that the effect of THC on DMN
activity as shown in the present study may depend on individual
genetic profiles, particularly of genes involved in dopamine
neurotransmission.
An increasing number of imaging studies use a pharmacological
challenge to study effects on cognition. For instance, the norepi-
nephrine/dopamine transporter inhibitor modafinil reduced DMN
activity during a simple visuomotor task. The modafinil effect in the
ventromedial prefrontal cortex was significantly correlated with
reaction time [68]. Treatment with methylphenidate normalized
DMN activity in off-methylphenidate ADHD patients who showed
attenuated DMN activity during low incentive conditions [69]. In
addition, nicotine administration decreased DMN activity at rest in
non-smokers [70], improved cognitive withdrawal symptoms of
abstinent smokers through modulation of functional connectivity
within the DMN and of inverse coupling between default mode and
central executive brain networks [71], and enhanced visuospatial
attention by deactivating DMN nodes including posterior cingulate
cortex and angular gyrus in minimally deprived smokers [15].
These studies provide converging evidence for an important role of
DMN in cognitive performance.
This study has several limitations. First, our experimental
paradigm focused on one aspect of executive function, namely
processing of continuously updated information. Current results
cannot be generalized over other aspects of executive function,
such as dual task execution, inhibition, and selective attention.
Second, although subjects were instructed to watch all stimuli that
were presented in the control task of our experimental paradigm,
this cannot be ensured as this task did not require a button press.
As a result, not only regions involved in executive function, but
also areas subserving motor and visual responses may be included
in the network of regions that were positively activated by the task.
It is unlikely, however, that this has affected our results as
individual regions included in this network did not show significant
effects of THC administration. Third, inclusion of incidental
cannabis users, as opposed to non-users, may affect interpretation
of results as previous cannabis use may influence the eCB system.
However, this seems less plausible as neither brain activity nor
behavioral effects were significantly correlated with reported
cannabis use (data not shown). Fourth, inclusion of both tobacco
users and non-users may affect interpretation of results as tobacco
use may influence DMN activity [15]. However, this is unlikely as
there was no significant difference in the effect of THC on DMN
activity between tobacco users and non-users, with both groups
showing a similar direction of the effect. In addition, tobacco users
did not show a significant correlation between the number of
cigarettes used per week and the effect of THC on DMN activity
(see Results S1). Fifth, the performance of ROI analyses implies
that we could have missed effects of THC administration in non-
task-specific areas or THC-induced shifts in activity within ROIs.
Finally, non-specific THC-induced changes on cerebral blood flow
may have confounded our results [72]. However, the correlation
between DMN activity and performance after THC administra-
tion indicates that effects are specifically related to task execution.
In conclusion, this study shows that THC administration results
in less deactivation in the DMN during an executive function task,
an effect that is correlated with task performance. These results
suggest an important role for the eCB system in both DMN
modulation and executive function. The association of the eCB
system with DMN modulation may be relevant for psychiatric
disorders associated with executive function deficits, such as
schizophrenia and ADHD, as well as for neurological disorders
such as Alzheimer’s disease.
Supporting Information
Figure S1 Effects of THC administration on activity in regions of interest (ROIs). The figure shows brain activity in A, TID regions, and B, TIA regions, after administration of placebo (white) and THC (black) (n = 20; mean 6 SEM). Full ROI names are given in Figure 3. TIA, task-induced activation;
TID, task-induced deactivation; a.u., arbitrary units.
(PDF)
Figure S2. Activity patterns during performance of CPT-IP (baseline: rest). The figure shows activity after
THC, Default Mode Network and Executive Function
PLOS ONE | www.plosone.org 8 July 2013 | Volume 8 | Issue 7 | e70074
administration of A, placebo, and B, THC (n = 20; t . |4.6|, p , 0.0001 uncorrected, clusters $ 10 voxels). (PDF)
Figure S3. Task performance in percentage of correct responses after placebo and THC administration (n = 20; mean ± SEM). THC administration significantly decreased the percentage of correct responses (from 90.5 6 1.7% to 85.0 6 2.5%, t(19) = 2.95, p = 0.008).
(PDF)
Results S1.
(PDF)
Acknowledgments
We would like to thank Dr David Fleck and Dr Stephen Strakowski for
kindly sharing the CPT-IP paradigm, Storz & Bickel for supplying the
Volcano vaporizer, and Annelies Brouwer, Erik Oudman, Joep van der
Graaf and Kim Noorman for their help with data acquisition and analysis.
Author Contributions
Conceived and designed the experiments: MGB HHH GJ NFR.
Performed the experiments: MGB HHH GJ. Analyzed the data: MGB
JMJ. Wrote the paper: MGB JMJ HHH GJ RSK NFR.
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