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Cannabis Addiction and the Brain: a Review
&Christopher Kure Liu
&Corinde E. Wiers
&Nora D. Volkow
Received: 18 January 2018 / Accepted: 7 March 2018 /Published online: 19 March 2018
#The Author(s) 2018
Cannabis is the most commonly used substance of abuse in the United States after alcohol and tobacco. With a recent increase in
the rates of cannabis use disorder (CUD) and a decrease in the perceived risk of cannabis use, it is imperative to assess the
addictive potential of cannabis. Here we evaluatecannabis use through the neurobiological model of addiction proposed by Koob
and Volkow. The model proposes that repeated substance abuse drives neurobiological changes in the brain that can be separated
into three distinct stages, each of which perpetuates the cycle of addiction. Here we review previous research on the acute and
long-term effects of cannabis use on the brain and behavior, and find that the three-stage framework of addiction applies to CUD
in a manner similar to other drugs of abuse, albeit with some slight differences. These findings highlight the urgent need to
conduct research that elucidates specific neurobiological changes associated with CUD in humans.
Keywords Substance use disorders .Dopamine .Marijuana .THC
Cannabis is the most commonly used substance of abuse in
the United States after alcohol and tobacco (Carliner et al.
2017). In the US, cannabis use increased from 4% to 9.5%
between 2001 and 2002 and 2012–2013 and the prevalence of
Cannabis Use Disorder (CUD) increased from 1.5% to 2.9%
in the same time (Hasin et al. 2015). Despite these increases in
cannabis use and CUD, attitudes towards cannabis use have
softened: adult and adolescent perceptions of cannabis use risk
have decreased since 2001 (Hasin et al. 2015;Carlineretal.
2017). These shifting attitudes have intergenerational conse-
quences as offspring of parents who are early-onset cannabis
users and who meet criteria for CUD are more likely to be-
come early-onset cannabis users themselves (Henry and
Augustyn 2017). Withincreases in cannabis use and decreases
in perceived risk, it is necessary to reevaluate the addictive
potential of cannabis (Carliner et al. 2017; Hasin 2018).
In this review, we explore the nature of cannabis addiction
through a prominent model of drug addiction (Koob and
Vo l k o w 2016). We first explain the model, which proposes a
dysregulation of motivational circuits in three stages of addic-
tion: binge/intoxication, withdrawal/negative affect, and pre-
occupation/anticipation. Second, we summarize empirical ev-
idence for preclinical and human studies on the acute and
long-term effects of cannabis use on the brain and behavior
(similar to those of other drugs of abuse). Third, we review
potential therapeutic agents for CUD that may provide further
evidence for dysregulation in motivational circuits in CUD.
After reviewing the acute and chronic effects of cannabis use
on the brain and behavior and treatment options for cannabis
abusers, we discuss whether there is empirical evidence that
the three stages of addiction apply to CUD (Fig. 1provides an
overview of the current literature supporting this model).
Theoretical Model of Addiction
Koob and Volkow (2016) define drug addiction as a
Bchronically relapsing disorder^marked by compulsive drug
seeking and intake, loss of control in limiting intake, and the
emergence of a negative emotional state when access to a drug
is prevented. This model proposes three stages of addiction
with disturbances in three major neurocircuits: the binge/
intoxication stage driven by changes in the basal ganglia; the
Laboratory of Neuroimaging, National Institute on Alcohol Abuse
and Alcoholism, National Institutes of Health, 10 Center Drive 31,
Room B2L124, Bethesda, MD 20892, USA
National Institute on Drug Abuse, National Institutes of Health,
Bethesda, MD 20892, USA
Journal of Neuroimmune Pharmacology (2018) 13:438–452
withdrawal/negative affect stage driven by changes in the ex-
tended amygdala; and the preoccupation/anticipation driven
by changes in the prefrontal cortex (PFC). Within these do-
mains, Koob and Volkow (2016) describe neuroadaptations in
18 subsystems including the ascending mesocorticolimbic do-
pamine system, corticotropin-releasing factor (CRF) in the
central nucleus of the amygdala, and corticostriatal glutamate
The binge-intoxication stage of addiction is characterized
by an excessive impulsivity and compulsivity to use drugs
despite negative consequences associated with such use.
This stage involves hyperactivation of the mesocorticolimbic
dopaminergic reward pathway of the brain associated with the
positive reinforcement of the rewarding effects of drugs. A
hallmark of the binge/intoxication stage is an impairment in
incentive salience, whereby drug-associated cues and contexts
associated with the initial exposure to a drug are attributed
exaggeratedly high rewarding properties and become condi-
tioned to elicit dopamine (DA) release. This incentive salience
dysfunction appears to drive DA signaling to maintain moti-
vation to take the drug upon exposure to conditioned-cues and
even when its pharmacological effects lessen, secondary to the
development of tolerance (Koob and Volkow 2016).
The withdrawal/negative affect stage is then triggered by
opponent-process responses following binge episodes. These
opponent-process responses are marked by within-systems
and between-systems neurobiological changes that drive the
loss of motivation towards non-drug rewards and impaired
emotion regulation seen in this stage. Within-systems
neuroadaptations include changes in the function of brain re-
ward systems including decreased dopaminergic signaling in
the nucleus accumbens (NAcc) and dorsal striatum that result
in an elevation of reward thresholds for non-drug reinforcers,
which contributes to amotivation. Between-systems
neuroadaptations include dysfunction of neurochemical sys-
tems that are not primarily involved in the rewarding effects of
drugs of abuse; this includes changes in brain systems in-
volved in stress responses such as increased CRF release in
the amygdala and HPA-axis dysfunction. The changes
resulting from opponent-processes responses drive character-
istic symptoms of a withdrawal symptom such as increased
anxiety-like responses, chronic irritability, malaise, and dys-
phoria during acute and protracted abstinence from a drug of
abuse (Koob and Volkow 2016).
The preoccupation/ anticipation stage is implicated in the
reinstatement of substance use following abstinence.
Executive control over craving and impulsivity iskey in main-
taining abstinence and is mediated by the PFC. The
preoccupation/anticipation stage is marked by dysregulation
of signaling between the PFC and areas of the brain that
Fig. 1 a. Model of neurocircuitry and correlating disruptions in brain function and neurophysiology that contribute to behaviors underlying drug
addiction. b. Summary of the changes in neurocircuitry associated with each stage
J Neuroimmune Pharmacol (2018) 13:438–452 439
Behavior THC-induced DA release disrupts incentive salience attribution (Koob and Mason 2016; Bhattacharyya et al.
2012; Wijayendran et al. 2016)
Acute THC leads to striatal DA release in animals and humans (Bloomfield et al. 2016; Bossong et al. 2015)
Chronic THC downregulates CB1Rs and blunts striatal DA release in
animals and humans
(Van De Giessen et al. 2017; Volkow et al.
2014; Scherma et al. 2016; Colizzi et al. 2016)
Imaging Correlates Heightened, THC-induced ventral striatal activation to losses in MID task
driven by chronic, relapsing cannabis users.
(Yip et al. 2014)
Chronic cannabis use associated with blunted DA response to reward
anticipation in the NAcc in MID task
(Martz et al. 2016)
It has been established that hyper-sensitivity to the rewarding properties of
drugs contribute to positive reinforcement, which is driven by disrupted
incentive salience processing
(Filbey et al. 2013)
Therapies Therapies with greatest reduction in binge-intoxication antagonize CB1Rs
and include: rimonabant, which blocks the intoxicating and tachycardic
effects of smoked cannabis
(Crippa et al. 2012, Danovitch and Gorelick
Partial agonists, which block the reinforcing effects of other drugs of abuse,
have the potential to reduce the effects of cannabis intoxication
(Koob and Mason 2016)
Strains with higher CBD to THC ratios reduce the appetitive effects of
cannabis compared to strains with lower CBD to THC ratios, suggesting
CBD as a potential treatment for acute cannabis intoxication
(Morgan et al. 2010)
Behavior Presence of withdrawal syndrome marked by: irritability, anxiety decreased
appetite, restlessness, and sleep disturbances
(Karila et al. 2014; Katz et al. 2014; Davis et
Increase in negative affect after prolonged cannabis use in adults and
(Dorard et al. 2008; Katz et al. 2014; Volkow
et al. 2014c; Heitzeg et al. 2015; Davis et al.
Presence of an amotivational state after prolonged cannabis exposure in
rhesus monkeys and humans
(Volkow et al. 2014a, 2016; Becker et al.
2014; Panlilio et al. 2015; Heitzeg et al. 2015)
In rodents, cannabis withdrawal is associated with an increase in CRF in
central nucleus of the amygdala
(Rodriguez de Fonseca et al. 1997; Caberlotto
et al. 2004; Curran et al. 2016)
In human studies, cannabis withdrawal seems to be related to HPA axis
(Somaini et al. 2012; Cuttler et al. 2017)
Imaging Correlates Chronic cannabis use is associated with decreased stimulant-induced DA
reactivity that is associated with greater negative emotionality
(Volkow et al. 2014c)
Chronic cannabis use and cannabis withdrawal are associated with affect
dysregulation related to amygdala functioning
(Filbey et al. 2013; Pujol et al. 2014; Heitzeg
et al. 2015; Spechler et al. 2015; Zimmermann
et al. 2017)
Therapies Therapies with the greatest reduction of withdrawal symptoms target CB1R
and include: oral THC, nabixmol, nabilone all of which have a lower abuse
potential than smoked cannabis
(Balter et al. 2014; Allsop et al. 2015; Tsang
and Giudice 2016; Brezing and Levin 2018)
Therapies that have shown the greatest reduction of withdrawal symptoms
and the lowest rates of relapse include naltrexone (a mu opioid receptor
antagonist), gabapentin (a GABA-a receptor agonist), and N-acetylcysteine
(Brezing and Levin 2018)
Behavior Preclinical and clinical models demonstrate impaired executive function in
domains of memory and IQ result from acute and chronic cannabis use.
Age-specific effects may be present.
(Koob and Volkow 2016; Renard et al. 2016;
Broyd et al. 2016; Becker et al. 2014; Volkow
et al. 2014a; Caballero and Tseng 2012)
No significant long-term effects of adolescent cannabis use on executive
function was found in several longitudinal co-twin cohort studies. Social
and environmental factors may explain poor executive function among
(Meier et al. 2017; Jackson et al. 2016)
Animal studies demonstrate increased glutamate transmission during drug
self-administration while animals receiving glutamate receptor antagonists
show reduced relapse rates.
(Caprioli et al. 2017)
Imaging Correlates Increased BOLD response to cannabis cues compared to naturally hedonic
cues in mesocorticolimbic regions among cannabis users.
(Filbey et al. 2016).
Positive correlations between cue-induced self-rated craving for cannabis
and BOLD responses within the mesocorticolimbic system and the insula.
(Filbey et al. 2016; Norberg et al. 2016)
Therapies N-acetylcysteine is a proposed anticraving agent as it acts on the cysteine-
glutamate antiporter to reduce glutamate neurotransmission that is
upregulated during withdrawal. Preliminary clinical studies have
demonstrated reduced craving and relapse rates in cannabis users.
(Asevedo et al. 2014; Samuni et al. 2013)
Fig. 1 (continued)
440 J Neuroimmune Pharmacol (2018) 13:438–452
control decision making, self-regulation, inhibitory control
and working memory and might involve disrupted
GABAergic and glutamatergic activity (Koob and Volkow
2016). Behaviorally, this translates into excessive salience at-
tribution to drug-paired cues, decreases in responsiveness to
non-drug cues and reinforcers, and decreases in the ability to
inhibit maladaptive behavior (Koob and Volkow 2016).
Acute Effects and Insight into Reinforcing/Addictive
Properties of Cannabis
All drugs of abuse increase DA release —a key neurobiolog-
ical process that generates their reinforcing effects (Koob and
Volkow 2016). Here we evaluate the acute changes in DA
circuitry associated with cannabis intake in preclinical and
clinical studies that provide basis for the reinforcing effects
of cannabis. While the two main constituents of cannabis are
delta9-tetrahydracannabinol (THC) and cannabidiol (CBD),
THC seems to be responsible for cannabis’addictive potential
due to its psychoactive properties and associated effects on
brain dopaminergic function. Acute THC administration
elicits striatal DA release in animals (Ng Cheong Ton et al.
1988) and humans (Stokes et al. 2010; Bossong et al. 2015;
Bloomfield et al. 2016). However, another study found no
evidence for THC-induced DA release (Barkus et al. 2011);
this may be because THC induces quantitatively less DA re-
lease than psychostimulants such as methylphenidate or am-
phetamine (Volkow et al. 1999a). Nonetheless, these findings
suggest that THC increases DA release similar to other drugs
Several animal models of cannabis exposure have been
established in rodents and non-human primates (Panlilio
et al. 2015). In studies with rodents, neurophysiological
methods such as intracranial microinjection, microdialysis,
and single-unit electrophysiological recording techniques are
used to study the acute effects of THC and other cannabinoids
in the brain directly (Oleson and Cheer 2012;Panlilioetal.
2015). Behavioral methods include the use of place condition-
ing, drug discrimination, intracranial self-stimulation, or intra-
venous self-administration to study the reinforcing effects of
cannabinoids in vivo (for further details see: Maldonado and
RodriguezdeFonseca2002; Tanda and Goldberg 2003;
Maldonado et al. 2011; Panlilio et al. 2015;Zandaand
Fattore 2018). Robust intravenous self-administration para-
digms in animals have been difficult to establish. That is, in
rodents THC is unable to sustain intravenous self-
administration (Lefever et al. 2014), whereas squirrel mon-
keys have found to self-administer THC; suggesting differ-
ences in species. However, other behavioral methodologies,
such as drug discrimination and conditioned place preference
paradigms, reveal the rewarding effect of THC and other can-
nabinoids (Maldonado and Rodriguez de Fonseca 2002;
Tanda and Goldberg 2003; Maldonado et al. 2011; Oleson
and Cheer 2012; Panlilio et al. 2015).
In rodents, THC-induced DA release is associated with
increased intracranial self-stimulation in key reward pathways
of the brain (Katsidoni et al. 2013). Likewise, low doses of a
cannabinoid-1 receptor (CB1R) agonist in the PFC increased
spontaneous firing and bursting rates of ventral tegmental area
(VTA) DA neurons, which was associated with potentiated
salience of fear memories in rats (Draycott et al. 2014). THC
elicits striatal DA release by activating CB1R, which are co-
localized with DA receptors in the striatum and substantia
nigra, regions implicated in salience processing
(Wijayendran et al. 2016). This suggests that the
endocannabinoid system (eCS) is involved in regulating DA
release during salience attribution (Bloomfield et al. 2016),
and that acute THC dysregulates the dopaminergic and
endocannabinoid systems which then leads to impairments
in salience processing (Wijayendran et al. 2016). These pre-
clinical findings may provide a biological basis for human
studies which show impaired salience processing after THC
administration. In one study, THC-potent cannabis was found
to increase attentional bias towards cannabis-related stimuli in
cannabis users during a computer-based dot-probe behavioral
task (Morgan et al. 2010). In a separate fMRI task, healthy
participants performed a visual oddball paradigm; THC ad-
ministration resulted in making non-salient stimuli appear
more salient (Bhattacharyya et al. 2012). Together, these
pre-clinical and clinical findings reveal that THC administra-
tion has reinforcing properties that alter salience processing
via increased dopaminergic signaling like other drugs of abuse
(Morgan et al. 2010; Bhattacharyya et al. 2012;Draycottetal.
2014; Wijayendran et al. 2016; Bloomfield et al. 2016).
Long-Term Effects of Cannabis: Behavior
Chronic cannabis use is associated with an increased risk of
developing substance use disorders (SUD); about 9% of
those who use cannabis present with characteristic symp-
toms of dependence according to DSM-IV criteria (Volkow
et al. 2014a). Diagnoses of cannabis abuse and dependence
in the DSM-IV did not include withdrawal due to uncer-
tainty of its diagnostic features (Katz et al. 2014)Inthe
DSM-5, however, cannabis abuse and dependence fall un-
der a diagnosis of CUD which now includes withdrawal
from cannabis. Withdrawal was added as a diagnostic
criteria for CUD as it is often accompanied by increased
functional impairment of normal daily activities similar to
those seen in other SUD (Karila et al. 2014;Katzetal.
2014; Davis et al. 2016). Symptoms of cannabis withdrawal
J Neuroimmune Pharmacol (2018) 13:438–452 441
also seem to appear in a similar time course and manner as
withdrawal from other substances (Karila et al. 2014).
A clinical diagnosis of cannabis withdrawal includes irrita-
bility, anger or aggression, nervousness or anxiety, sleep dif-
ficulty, decreased appetite or weight loss, restlessness, de-
pressed mood, and physical symptoms causing significant
discomfort such as shakiness or tremors, sweating, fever,
chills, and headaches (Karila et al. 2014;Katzetal.2014).
Typically, symptoms of cannabis withdrawal occur 1 to 2 days
after cessation of heavy use and can last between 7 and 14 days
(Davis et al. 2016). The most common symptoms observed
during cannabis withdrawal include irritability, anxiety, de-
creased appetite, restlessness, and sleep disturbances (Oleson
and Cheer 2012; Panlilio et al. 2015; Curran et al. 2016;Gates
et al. 2016). Sleep disturbances seem to be characterized by
trouble falling asleep, decrease in total sleep time, and the
presence of nightmares and strange dreams (Gates et al.
2016). The severity of withdrawal symptoms was associated
with greater negative impact on normal, daily activities (Davis
et al. 2016) suggesting that the effects of cannabis withdrawal
seem to parallel withdrawal in other drugs of abuse.
Koob and Volkow (2016) posit that the withdrawal stage of
addiction is marked by an increase in negative affect which
also seems to be the case for cannabis addiction (Volkow et al.
2014c). In addition to acute withdrawal-related emotional dis-
turbances such as irritability and anxiety (Katz et al. 2014;
Davis et al. 2016), prolonged cannabis use is associated with
long-term affect dysregulation. In a longitudinal study of ad-
olescents, cannabis users consistently reported greater nega-
tive emotionality than healthy controls between the ages of 13
and 23; moreover, as healthy controls showed a decrease in
negative emotionality with age, negative emotionality
remained elevated for cannabis users during over the same
time (Heitzeg et al. 2015). Another study of adolescents found
that half of a group of adolescents undergoing treatment for
cannabis withdrawal had at least one comorbid diagnosis of
anxiety or depression; additionally, for these adolescents
greater cannabis use was associated with increased depressive
and anxiety-like symptoms (Dorard et al. 2008).
These changes in the affective state after prolonged canna-
bis use may also influence motivation. In both rhesus mon-
keys and humans, withdrawal from cannabis seems to involve
the presence of an amotivational state (Karila et al. 2014;
Panlilio et al. 2015; Volkow et al. 2014a,b,c,2016). The
amotivational state has been previously described as a
Breduced motivation and capacity for usual activities required
for everyday life, a loss of energy and drive to work and
personality deterioration^(Karila et al. 2014). The origin of
this amotivational state is still unknown and may be related to
changes in executive function (Karila et al. 2014) and to re-
duced dopamine signaling after chronic cannabis use
(Bloomfield et al. 2014; Volkow et al. 2014c). In rhesus mon-
keys, chronic cannabis smoke exposure was associated with
lower motivation scores in a place conditioning paradigm,
although these effects disappeared two to three months after
cessation of the cannabis treatment (Paule et al. 1992). In one
study of neurocognition, chronic cannabis users demonstrated
impairments in verbal memory, spatial working memory, spa-
tial planning, and motivated decision-making compared to
healthy controls (Becker et al. 2014). These findings suggest
that the amotivational state during withdrawal may be related
to cognitive dysfunction and to reduced dopamine signaling
after chronic cannabis use.
Cognitive dysfunction, specifically impairments in execu-
tive domains, after chronic cannabis use is a key feature of the
neurobiological model of addiction (Koob and Volkow 2016).
Deficits in executive function after chronic cannabis use have
been shown in both preclinical and clinical studies. In one
preclinical study, chronically administering a synthetic canna-
binoid agonist to adolescent rats impaired short-term working
memory in adulthood (Renard et al. 2016). Specifically, this
chronic cannabinoid exposure altered PFC structure and im-
paired cortical synaptic plasticity from reduced long-term po-
tentiation (LTP) in the hippocampus-PFC circuit. These find-
ings support the theory that adolescent cannabis use causes
lasting deficits in memory. However, they are likely age-
specific effects as preclinical and clinical studies have demon-
strated a lackof long-lasting cognitive impairments from adult
chronic cannabis use (Renard et al. 2016).
Many clinical studies have investigated the long-term ef-
fects of chronic cannabis use on markers of executive function
such as IQ, verbal learning, and memory. The results are
varied and equivocal, as longitudinal studies with controlled
confounds are difficult to establish. Volkow et al. (2014a,b,c)
report that cannabis use during adolescence and young adult-
hood is associated with impaired functional connectivity in the
brain and corresponding declines in IQ. A 2016 systematic
review of 105 papers assessing the acute and chronic effects
of cannabis on human cognition found that memory has been
the most consistently impaired cognitive measure (both after
acute and chronic cannabis use), with the strongest effects in
the verbal domain (Broyd et al. 2016). The evidence for im-
pairments in other domains of executive function such as rea-
soning, problem solving, and planning was less conclusive, as
numerous studies found no significant differences in case-
control comparisons. However, studies examining heavy
users as well as early-onset users reported impaired executive
function, especially when the sample was predominantly older
participants (Becker et al. 2014; Broyd et al. 2016). This may
suggest a conditional effect, unique to adolescent and heavy
cannabis users while moderate and adult users are less vulner-
able to the harmful effects of cannabis on cognition.
Despite earlier findings of impaired executive functioning
in adolescent- and early- onset users, it is important to note
that several recent studies found no significant long-term ef-
fects of adolescent cannabis use on executive function. Meier
442 J Neuroimmune Pharmacol (2018) 13:438–452
et al. (2018) report a longitudinal co-twin control study that
showed no significant association between adolescent canna-
bis use and neuropsychological decline, and instead suggest
social and environmental factors as explanations for poor ex-
ecutive function among cannabis users. This study was par-
ticularly insightful because of a large sample size (n=1989)
and IQ assessments prior to the onset of cannabis use (IQ
obtained at age 5, 12, and 18). It demonstrated that adolescents
who used cannabis had a lowerchildhood IQ and a lower IQ at
18 than non-users, but that there was no decline in IQ from
pre- to post-cannabis use (Meier et al. 2018). These results are
in line with another co-twin longitudinal study that investigat-
ed two large cohorts of twins and found no significant differ-
ence in IQ change over time between twins discordant for
cannabis use (Jackson et al. 2016). However, lower baseline
IQ was associated with adolescent cannabis use suggesting
that social and environmental factors influence an adolescent’s
subsequent cannabis use (Jackson et al. 2016). Together, these
studies suggest that lower IQ may be a risk factor for cannabis
abuse rather than the use of cannabis resulting in neuropsy-
chological decline. However findings on the effects of canna-
bis exposure during adolescents are controversial and require
investigation with prospective designs that take advantage of
brain imaging technologies. The ABCD study, a prospective
study that aims to follow 10,000 children as they transition
into adulthood with a detailed phenotypic characterization in-
cluding periodic brain imaging, would help clarify what ef-
fects cannabis consumption might have on brain develop-
ment, neurocognitive function and mental illness (Volkow
et al. 2017b).
Long-Term Effects of Cannabis: Neurophysiological
The chronic relapsing nature of addiction seems to involve
underlying neurophysiological changes in reward, stress, and
executive function circuits (Koob and Volkow 2016). Here we
summarize findings about the effects of chronic cannabis use
on these circuits.
Chronic cannabis abuse is modeled in animals with repeat-
ed treatments of cannabis (through smoke exposure) or THC
and other cannabinoids (typically intravenous injections).
Neurophysiological changes after these different methods of
chronic cannabis treatment are then typically measured
through electrophysiological recordings and microdialysis
(Maldonado and Rodriguez de Fonseca 2002; Tanda and
Goldberg 2003; Maldonado et al. 2011; Oleson and Cheer
In rats, early-life exposure to THC blunts dopaminergic
response to naturally rewarding stimuli that elicit DA release
later in life (Bloomfield et al. 2016). Likewise in rats, adoles-
cent exposure to THC resulted in increased self-administration
of and blunted striatal DA response to CB1R agonists in
adulthood (Scherma et al. 2016). Changes in reward-related
circuitry after chronic cannabis use may be related to changes
in the eCS after prolonged cannabis use. The eCS has been
implicated in reward-processing and reward-seeking behavior
given that CB1 receptors are densely expressed in areas asso-
ciated with reward processing and conditioning including the
amygdala, cingulate cortex, PFC, ventral pallidum, caudate
putamen, NAcc, VTA, and lateral hypothalamus (Parsons
and Hurd 2015; Volkow et al. 2017a). In animals, activation
of CB1 receptors seems to influence the hedonic effects of
natural rewards after THC administration, suggesting that can-
nabis can affect reward sensitivity via activation of CB1 re-
ceptors (Parsons and Hurd 2015).
Chronic THC exposure has further been shown to down-
regulate CB1Rs, providing a neurobiological basis for the
development of tolerance and desensitization to the rewarding
effects of THC (Colizzi et al. 2016). In rodents, chronic ad-
ministration of THC or CB1R agonists leads to tolerance in
most responses as well as a decrease in CB1R availability in
many brain areas (Maldonado and Rodriguez de Fonseca
2002; Tanda and Goldberg 2003; Maldonado et al. 2011). In
cannabis users, withdrawal symptoms have also been associ-
ated with reductions in CB1R availability as assessed by
C]OMAR PET imaging (Curran et al. 2016;D’Souza
et al. 2016). Hirvonen et al. (2012) found that cannabis use
downregulates CB1R in cortical regions, potentially altering
the brain’s reward system. However, they also found that after
4 weeks of abstinence, CB1R density returned to normal in
cannabis users in all regions except the hippocampus. This
suggests that some neurobiological changes of chronic canna-
bis use are reversible (Hirvonen et al. 2012).
Chronic cannabis use and administration is also associated
with neurophysiological changes in stress responsivity. In ro-
dents, the neurophysiological changes associated with canna-
bis withdrawal are modeled through precipitated withdrawal
through the use of rimonabant (a selective CB1R blocker)
after repeated cannabinoid treatment (Maldonado et al. 2011;
Oleson and Cheer 2012; Panlilio et al. 2015). Cannabinoid
withdrawal in rodents is associated with an increase in the
stress peptide CRF in the central nucleus of the amygdala
(Rodriguez de Fonseca et al. 1997; Maldonado et al. 2011;
Panlilio et al. 2015; Curran et al. 2016), which suggests the
presence of between-systems changes in brain stress systems,
as described by the Koob and Volkow model (2016). In addi-
tion, the eCS seems to be involved in regulating the stress
response through its action on the amygdala and HPA axis
(Dow-Edwards and Silva 2017; Volkow et al. 2017a). The
eCS modulates interactions between the PFC, amygdala, and
hippocampus which are all involved in emotional memory,
anxiety-related behaviors, and drug cue-induced craving in
SUD (Jasinska et al. 2014). Additionally, endocannabinoids
seem to be required for feedback to normal stress responses:
glucocorticoids increase the endogenous cannabinoids
J Neuroimmune Pharmacol (2018) 13:438–452 443
anandamide (AEA) and 2-acylglycerol (2-AG) in the
paraventricular nucleus while CB1R antagonists increase
HPA axis output. In rodents, exogenous cannabinoids seem
to create a dysregulation of stress responsivity and anxiety-
related behaviors (Dow-Edwards and Silva 2017).
Moreover, chronic cannabis abuse is associated with the
dysregulation of stress responsivity in humans (Curran et al.
2016). Studies in cannabis users show that chronic cannabis
use is related to both blunted and hyperactive stress responses
(Somaini et al. 2012; Cuttler et al. 2017). Cuttler et al. (2017)
found that healthy controls had an increase in cortisol levels
under a stress-provoking condition compared to baseline but
did not find the same increase in active cannabis users. In
another study, both active and abstinent cannabis users had
persistent hyperactivity of the HPA axis (measured by blood
cortisol and ACTH levels) compared to healthy controls
(Somaini et al. 2012). This pattern of HPA axis dysregulation
is also seen in alcohol users: chronic alcohol use seems to
attenuate the cortisol response to acute psychological stimula-
tion of the HPA axis, but is related to elevated cortisol levels
during alcohol intoxication and abstinence in dependent users
(Stephens and Wand 2012).
In addition to its role in HPA axis dysfunction and reward
processing, the hyperactivation of the eCS may also play a
role in the executive dysfunction sometimes observed in can-
nabis use. The eCS is highly active in adolescent brain devel-
opment, particularly in the PFC, a region that exercises exec-
utive function (Dow-Edwards and Silva 2017). Exogenous
cannabinoids hyperactivate CB1 receptors which are
expressed in pyramidal neurons and GABAergic interneu-
rons, indicative of the regulatory role of the eCS in GABA
and glutamate neurotransmission (Caballero and Tseng 2012;
Volkow et al. 2017a). Activation of presynaptic CB1 receptors
inhibits glutamate transmission onto GABAergic cells in the
PFC, reducing the function of inhibitory prefrontal circuits.
Therefore, hyperactivation by exogenous cannabinoids during
development could disrupt the maturation of GABAergic in-
terneurons in the PFC and desynchronize PFC circuits
(Caballero and Tseng 2012). Thus, adolescent cannabis use
may affect brain development and result in enduring alter-
ations in the GABA/glutamate balance in the PFC (Renard
et al. 2016).
Neuroadaptations in glutamatergic signaling resulting from
repeated cannabis use are likely also implicated in periods of
cannabis abstinence and craving (Samuni et al. 2013). This
theory is supported by a review of animal studies that demon-
strated increased glutamate signaling during drug self-
administration and relapse, offering a potential neurochemical
target for treatment in preventing craving and subsequent re-
lapse. For example, rodent and nonhuman primate models
receiving periodic injections of glutamate receptor antagonists
have shown a reduction in relapse rates (Caprioli et al. 2017).
Nonetheless, these findings need to be corroborated in rodents
since there is conflicting evidence for whether self-
administration in rodent models provides robust evidence of
THC as a behavioral reinforcer (Tanda and Goldberg 2003;
Maldonado et al. 2011;Panlilioetal.2015; Melis et al. 2017).
Long-Term Effects of Cannabis on the Brain:
Addiction is a recurring cycle that worsens over time and
involves neuroplastic changes in the brain reward, stress,
and executive function systems (Koob and Volkow 2016).
Previous neuroimaging studies reveal the long-term effects
of chronic cannabis use on several different brain systems
including the reward, endocannabinoid, and stress systems
as well as brain areas involved in emotion processing and
Similar to animal models of chronic THC exposure, chron-
ic cannabis use has been shown to blunt DA response to DA-
releasing stimulant drugs in the striatum with both [
PHNO and [
C]raclopride PET imaging (Volkow et al.
2014c;Bloomfieldetal.2016; van de Giessen et al. 2017)
and to decrease DA synthesis as assess with PET imaging with
F]DOPA (Bloomfield et al. 2014)(Fig.2). This pattern of
decreased stimulant-induced DA release is also seen with
chronic use of other drugs of abuse such as alcohol, cocaine,
and nicotine (Koob and Volkow 2016). However, cannabis
users do not show lower baseline D2/D3 receptor availability
in the striatum compared to healthy controls –a pattern seen in
chronic alcohol, nicotine, cocaine, opiate and methamphet-
amine users (Volkow et al. 1996b,2001,2002,2014b,
2017c;Wangetal.1997;Martinezetal.2012; Tomasi et al.
2015b; Wiers et al. 2016a,2017; Ashok et al. 2017).
Moreover, the stimulant challenge led to significantly lower
self-reported ratings of feeling high (Volkow et al. 2014c), and
decreased brain glucose metabolism in the striatum, thalamus,
andmidbrain(Wiersetal.2016b) in cannabis users versus
controls. Cannabis users had higher negative emotionality
and lower positive emotionality personality scores than con-
trols, and negative emotionality scores were inversely corre-
lated with methylphenidate-induced dopamine increases in
the ventral striatum (Volkow et al. 2014c; Wiers et al.
2016b). These findings offer an explanation for decreased
dopamine reactivity in the striatum during abstinence that
may contribute to negative emotionality, which is consistent
with lower reward sensitivity in cannabis users during the
withdrawal phase of addiction (Volkow et al. 2014c). In an-
other study, a stimulant challenge also led to blunted brain
glucose metabolism in striatal regions, which was associated
with craving (Wiers et al. 2016b). Together these findings
from stimulant challenges indicate functional changes in the
dopaminergic reward system in chronic cannabis users.
Furthermore, fMRI studies have also revealed functional
and structural changes in brain areas involved in reward
444 J Neuroimmune Pharmacol (2018) 13:438–452
processing after chronic cannabis use. In one study, partici-
pants in a cannabis-dependent group had greater activation in
the ventral striatum in response to losses during a monetary
incentive delay (MID) task compared to healthy controls (Yip
et al. 2014). Compared to controls, the cannabis-dependent
participants also had smaller putamen volumes, a brain region
involved in habit formation. These differences seemed to be
driven by participants who were unable to stay abstinent from
cannabis and were comparable to findings in tobacco smokers
suggesting similar changes in reward functioning in both to-
bacco and alcohol addiction (Yip et al. 2014). In another fMRI
study with the MID task, cannabis users in withdrawal had
greater activation in the ventral striatum in response to posi-
tive incentives compared to healthy controls during the MID
task, similar to findings in alcohol users (Filbey et al. 2013).
Persistent cannabis use also seems to be related to a blunted
response to reward anticipation in the NAcc during the MID
task: in this study, even after controlling for prior and current
use of other drugs, greater cannabis use was related to de-
creased activation in the NAcc during reward anticipation at
baseline, 2 year, and 4 year follow ups (Martz et al. 2016).
Together, these findings suggest that chronic cannabis use
produces functional alterations in areas involved in reward
A recent fMRI study investigated whether cannabis use
sensitizes and disrupts the mesocorticolimbic reward process-
es during a hedonic cue-reactivity task. A cohort of chronic
cannabis users (requiring 72 h of abstinence) showed greater
BOLD response for cannabis cues compared to natural reward
cues (fruit) in the orbitofrontal cortex (OFC), striatum, anterior
cingulate gyrus, and VTA, regions along the
mesocorticolimbic-reward pathway (Filbey et al. 2016). In
cannabis users, there were also significant positive correla-
tions between cue-induced self-rated craving for cannabis
and BOLD responses within the mesocorticolimbic system
and in the insula. The latter data supports the addictive model
of cannabis as insula activation may serve as a biomarker to
help predict relapse (Filbey et al. 2016). This brain region
contributes to interoceptive awareness of negative emotional
states and is differentially activated during craving (Koob and
Vo l k o w 2016). This is also consistent with prior findings that
the dopaminergic reward system is reactivated during acute
craving episodes (Volkow et al. 1999b,2005;Kooband
Vo l k o w 2016). Moreover, in cannabis abusers, but not in con-
trols, acute THC intoxication elicited activation of brain re-
ward regions as assessed by increases in brain glucose metab-
olism in striatum and orbitofrontal cortex (Volkow et al.
1996a). Overall, these studies demonstrates that chronic can-
nabis use sensitizes the mesocorticolimbic-reward system to
cannabis cues and to THC (Volkow et al. 1996a; Filbey et al.
2016). These findings suggest that chronic cannabis use af-
fects key brain circuits involved in the reward system similar
to other drugs of abuse.
In addition to changes in reward processing, chronic can-
nabis use also seems to affect emotion processing. Several
MRI studies reveal functional and structural differences in
the amygdala –a key brain structure in processing emotions
–after chronic cannabis use. Compared to healthy controls,
adolescents who used cannabis had lower activation in the
amygdala in an emotional arousal word task during fMRI
(Heitzeg et al. 2015). However, in another fMRI study, ado-
lescent cannabis users showed greater amygdala activation to
angry faces compared to controls (Spechler et al. 2015).
Another study of facial emotion recognition found that during
abstinence, cannabis-dependent patients performed signifi-
cantly worse than controls in the identification of negative
emotions suggesting a lasting impact on emotion recognition
after chronic cannabis use (Bayrakçi et al. 2015). Together,
these fMRI findings indicate that chronic cannabis use alters
Fig. 2 a. Statistical group differences in the effect of methylphenidate on
the distribution volume between controls and marijuana abusers.
Methylphenidate-induced decreases in distribution volumes were stron-
ger in controls than in marijuana abusers (p< 0.005). There were no
regions where marijuana abusers showed greater decreases than controls.
b. Individual distribution volume values in putamen after placebo (PL)
and after methylphenidate (MP) for marijuana abusers and controls.
*p<0.05, **p< 0.005. (Figure adapted with permission from Volkow
et al. 2014a,b,c)
J Neuroimmune Pharmacol (2018) 13:438–452 445
The association between amygdala structure and cannabis
use is relatively unclear. Some studies have found morpholog-
ical and volumetric differences in the amygdala between
healthy controls and cannabis users in both adolescent and
adult cohorts (Gilman et al. 2014; Lorenzetti et al. 2015). On
the other hand, other studies that controlled for alcohol and
tobacco use found no differences in amygdala volume or
shape between cannabis users and healthy controls (Weiland
et al. 2015; Manza et al. 2018). A longitudinal study with
cannabis users and healthy controls found no volumetric dif-
ferences in gray matter at baseline or a three-year follow up
(Koenders et al. 2016). Despite these inconclusive structural
MRI findings, there is evidence that chronic cannabis use may
contribute to emotional dysregulation through functional
changes in the amygdala (Heitzeg et al. 2015; Spechler et al.
Further evidence of emotion dysregulation after chronic
cannabis use is seen in fMRI functional connectivity studies
with cannabis users (Pujol et al. 2014; Zimmermann et al.
2018). In one study, cannabis users showed increased
resting-state functional connectivity between posterior cingu-
late cortex (PCC) and other regions of the default mode net-
work (including angular gyri, medial and lateral PFC, ACC
and temporal cortex), and an anticorrelation between PCC
activation and insula activation. These connectivity patterns
were associated with a reduction in anxiety scores suggesting
an alteration of affect state that is related to changes in brain
function during cannabis addiction. As the insula is involved
in integrating interoceptive information for emotion, these
findings suggest that cannabis may enhance visceral sensa-
tions via insula activation to modify affect state (Pujol et al.
2014). Additionally, these resting-state functional connectivi-
ty patterns lasted one month after cessation of cannabis use
suggesting long-lasting changes in brain function after chronic
cannabis use (although functional connectivity patterns in
other networks normalized with abstinence, see Pujol et al.
2014). In another fMRI study, cannabis-dependent subjects
completed task and resting state fMRI 28 days after abstinence
(Zimmermann et al. 2018). During the task, in which partici-
pants were passively exposed to pictures of negative and neu-
tral valence, negative emotional stimuli elicited larger in-
creases in medial orbitofrontal cortex (mOFC) activity in
cannabis-dependent users than in healthy controls; researchers
also found greater functional connectivity between the mOFC
and dorsal striatal region as well as the mOFC and amygdala
compared to healthy controls during the task. Given that the
mOFC is a region implicated in emotional regulation, these
connectivity findings suggest the existence of persistent emo-
tional processing alterations in cannabis-dependent users even
after cessation of cannabis use (Zimmermann et al. 2018).
In addition to contributing to emotion dysregulation, ces-
sation of chronic cannabis use is associated with the develop-
ment of craving (Davis et al. 2016). Cue-reactivity is a
neurobiological metric to evaluate cue-induced craving, a
strong predictor of relapse for substance use (Budney et al.
2008; Wilson and Sayette 2015). A 2016 meta-analysis of
cue-reactivity in regular cannabis users reported moderate to
extreme cue-reactivity despite self-reports of low craving
(Norberg et al. 2016). These results may indicate that cannabis
users underestimate their own excessive salience, suggesting
that self-reports may not accurately reflect cannabis craving
intensity. Thus, excessive salience attribution to cannabis-
related cues appears to be a hallmark of cannabis addiction.
These studies further demonstrate the importance of collecting
objective measures of craving when studying the effects of
chronic cannabis use.
Finally, one of the most consistent neuroimaging findings
in addiction is that of dysregulation of frontal cortical regions
involved with executive function including the dorsolateral
prefrontal cortex, the ACC and the inferior frontal cortex.
Imaging studies investigating brain glucose metabolism,
which serves as a marker of brain function, reported decreased
frontal metabolism in cannabis abusers when compared with
controls (Sevy et al. 2008;Wiersetal.2016b)andin
polysubstance users who consumed cannabis (Moreno-
Lopez et al. 2012).
Treatments for CUD seem to target aspects of the binge-intox-
ication, withdrawal-negative affect, and preoccupation-
anticipation stages described by Koob and Volkow (2016).
Pharmacological treatments for the binge-intoxication
stage of cannabis addiction have focused on cannabinoid re-
ceptors. One mechanism of action involves direct antagonism
of CB1Rs. CB1R selective antagonists such as rimonabant
have been shown to block the subjective intoxicating and
tachycardic effects of smoked cannabis (Crippa et al. 2012;
Danovitch and Gorelick 2012). Despite the potential acute
benefits, direct antagonism with rimonabant is associated with
anxiety and depression (Taylor 2009; Danovitch and Gorelick
2012). Up to 10% of patients experienced anxiety and depres-
sion following use of rimonabant (Food and Drug
Administration 2007). Another downfall of this therapy is that
in order to avoid precipitated withdrawal, participants are re-
quired to abstain from drug use prior to administration of
antagonist medications, leading to poor compliance rates
(Vandrey and Haney 2009). While partial agonists have been
proposed to block the reinforcing effects of other drugs of
abuse like opioids and nicotine (Koob and Mason 2016), no
partial agonists have been found to reduce cannabis use.
Many different pharmacological treatments have been in-
vestigated for reduction of cannabis withdrawal symptoms,
primarily through modulation of cannabinoid receptors but
also through other neurotransmitter systems including
446 J Neuroimmune Pharmacol (2018) 13:438–452
glutamate, dopamine, norepinephrine, serotonin, and GABA
(Balter et al. 2014; Levin et al. 2016; Brezing and Levin
2018). In their comprehensive review of the different pharma-
cological treatments for CUD and cannabis withdrawal,
Brezing and Levin (2018) conclude that therapies targeting
specific symptoms of withdrawal (such as anxiety, irritability,
sleep disturbances, and decreased appetite) should be admin-
istered in conjunction with treatments that target reduction in
cannabis use and prevention of relapse. Promising candidates
for treatment of CUD that prevent relapse include naltrexone,
gabapentin, and N-acetylcysteine (NAC) (Mason et al. 2012;
Brezing and Levin 2018). The greatest reduction in multiple
withdrawal symptoms has been shown with treatment using
CB1R agonists such as dronabinol (oral THC), nabixmols (a
combination of THC and CBD), and nabilone (Balter et al.
2014; Brezing and Levin 2018); surprisingly, previous studies
have not shown cannabidiol as a potential treatment for can-
nabis withdrawal despite its anxiolytic effects (Brezing and
Levin 2018). With CB1R agonists as potential treatments, it
is necessary to consider the abuse potential of these drugs.
Dronabinol, nabilone, and nabixmols seem to have a lower
abuse potential than smoked cannabis (Allsop et al. 2015;
Tsang and Giudice 2016), but in one study of cannabinoid
replacement therapy, dronabinol and nabixmol had higher
self-reports of liking than placebo drugs (Allsop et al. 2015).
NAC is being investigated as an anticraving agent in can-
nabis addiction therapy due to its regulatory role in glutamate
and dopamine signaling (Samuni et al. 2013). NAC helps
regulate the intra- and extracellular levels of glutamate
through the cysteine-glutamate antiporter. Increased extracel-
lular glutamate levels activate inhibitory metabotropic gluta-
mate receptors, reducing glutamate neurotransmission
(Samuni et al. 2013). The upregulation of glutamate signaling
during the anticipation/preoccupation phase may be
counteracted with NAC treatment, reducing clinical symp-
toms of craving and therefore reducing relapse rates. A 2014
review article summarizes two studies that evaluated NAC
therapy in CUD. In one study, the placebo cohort reported
twice as many positive urine cannabinoid tests as compared
to the NAC cohort (Asevedo et al. 2014). The other study did
not report group differences in positive urine tests, but did find
a significant reduction in self-reported cannabis craving in the
treatment group (Asevedo et al. 2014). These studies reinforce
the role of glutamate upregulation during cannabis abstinence
on clinical outcomes such as craving and relapse.
After examining the acute and long-term effects of cannabis,
CUD appears to conform to the general patterns of changes
described in the Koob and Volkow model of addiction.
Previous preclinical and clinical studies indicate that features
of the three stages of drug addiction described by Koob and
Volkow are also present in cannabis addiction (Fig. 1), al-
though these findings may not be as robust as other drugs of
As described in the Koob and Volkow model (2016), most
drugs of abuse result in the hyperactivation of the
mesocorticolimbic dopaminergic reward pathway in the
binge-intoxication stage of addiction. This hyperactivation
seems to be present in cannabis addiction but to a lower ex-
tent. Acute THC administration elicits striatal DA release in
animals (Bloomfield et al. 2016) and THC challenges were
shown to increase striatal DA transmission in humans (Stokes
et al. 2010; Bossong et al. 2015); although other studies have
found no THC-induced increases in striatal DA (Barkus et al.
2011; Urban et al. 2012). Additionally, there are no baseline
differences in dopamine D2/D3 receptor availability between
cannabis users and healthy controls (Volkow et al. 2014c;van
de Giessen et al. 2017), a finding that does not parallel addic-
tion to other drugs of abuse (including cocaine, alcohol, meth-
amphetamine, nicotine, or heroin) which is associated with
substantial reductions in D2R availability in the ventral stria-
tum (Wang et al. 1997; Volkow et al. 2001,2014c,2017c;
Martinez et al. 2012;Albrechtetal.2013;Tomasietal.
2015a; Wiers et al. 2016a; Ashok et al. 2017). Nonetheless,
as with other drugs of abuse, chronic cannabis use still results
in blunted dopamine reactivity to a stimulant challenge
(Volkow et al. 2014c; van de Giessen et al. 2017).
This blunted stimulant-induced dopamine reactivity has
been associated with negative emotionality (Volkow et al.
2014c) a key feature of withdrawal/negative affect stage de-
scribed by Koob and Volkow (2016). With the addition of
withdrawal as a symptom of CUD, it is evident that the devel-
opment of cannabis addiction parallels addiction to other
drugs of abuse. In addition, chronic cannabis use has been
associated with affect dysregulation that may involve changes
in amygdala functioning (Filbey et al. 2013; Heitzeg et al.
2015; Spechler et al. 2015). As with other drugs of abuse,
cannabis seems to disrupt HPA axis function (Somaini et al.
2012; Cuttler et al. 2017), another key neuroadaptation of the
withdrawal/negative affect stage described by Koob and
Vo l k o w ( 2016).
Chronic cannabis use is also associated with the presence
of cannabis cue-induced craving after abstinence (Filbey et al.
2016; Norberg et al. 2016), a hallmark of the preoccupation/
anticipation stage of the Koob and Volkow framework (2016).
The presence of cannabis cue-induced craving seems to be
related to the loss of executive control over excessive salience
for cannabis (Norberg et al. 2016). In addition, chronic can-
nabis use has been linked to impaired memory and IQ, sug-
gesting changes in executive functioning after chronic canna-
bis use. However, IQ deficits appear to be present before ini-
tiation of cannabis use which may suggest that lower IQ could
be a risk factor for cannabis addiction (Jackson et al. 2016).
J Neuroimmune Pharmacol (2018) 13:438–452 447
Interestingly, chronic cannabis use is associated with a
downregulation of CB1R –THC’s target receptor –that is
restored after 4 weeks of abstinence in humans (Hirvonen
et al. 2012). This pattern of abstinence-induced changes in
target receptor density is also seen after abstinence from other
drugs of abuse such as heroin, stimulants, and alcohol (in
humans and animals) but with some caveats: the changes
found are not consistent across brain regions for every drug
and abstinence periods are not congruent between studies
(Wang et al. 2012; Seip-Cammack et al. 2013; Ashok et al.
2017; Volkow et al. 2017c). Future studies should examine to
whether changes in target receptors after abstinence are com-
parable across brain regions and if they follow the same time
course in CUD and other SUD.
Future studies should also investigate if there are other
features of the addiction framework proposed by Koob and
Volkow in cannabis addiction. Specifically, more longitudinal
studies should investigate behavioral and mood changes (such
as changes in IQ or the presence of a mood disorder) before
and after the onset of cannabis use to determine whether var-
iations in behavior and mood are risk factors or the result of
cannabis addiction rather than a consequence. Additionally,
with the increasing potency of THC in street cannabis
(ElSohly et al. 2016), it is necessary to evaluate whether
long-term changes may be related to the THC content of can-
nabis. Future studies should also investigate the specific
neurocircuitry Koob and Volkow (2016) implicate in the three
stages of addiction: specifically, how cannabis use impacts
glutamate signaling in the VTA (disrupted during binge/intox-
ication) and PFC (disrupted during preoccupation/craving)
and acetylcholine signaling in the habenula (disrupted during
Future research should also consider whether THC’s ef-
fects on neurons and microglia are related to addiction.
Previous research indicates that chronic THC exposure in an-
imals seems to activate microglia and produce neuroinflam-
mation that may underlie some of the cognitive deficits asso-
ciated with CUD (Melis et al. 2017); additionally, changes in
neuron and glia morphology after chronic cannabis exposure
may also contribute to the persistent cognitive and behavioral
deficits linked to CUD (Cutando et al. 2013;Kolbetal.2018).
Therefore, future studies should investigate whether chronic
THC exposure in animals and humans is linked to changes in
various cell types in the brain that contribute to cannabis ad-
diction through neuroinflammation. THC has also been
shown to have immunosuppressant properties in animals
(Suarez-Pinilla et al. 2014) while cannabis use has been asso-
ciated with adverse cardiovascular effects in humans (Pacher
et al. 2017; Goyal et al. 2017; Thomas et al. 2018); these
peripheral effects could be another line of future research.
Although further research is necessary (Box 1), the find-
ings summarized here indicate that neurobiological changes in
CUD seem to parallel those in other addictions, albeit to a
lesser extent in some brain systems. This is critical in light
of recent findings demonstrating an increase in cannabis use
and CUD and a corresponding decrease in the perceived risk
of cannabis (Carliner et al. 2017;Hasin2018).
Box 1. Questions for future research
•Do changes in CBIR density after abstinence from cannabis parallel
changes in target receptors of other drugs of abuse?
•Are behavioral and mood variations associated with cannabis use a risk
factor or consequence of cannabis addiction?
•Are long-term behavioral and neurophysiological changes related to the
THC content in cannabis?
•Is cannabis use associated with long-term changes in glutamate
signaling as seen in other drugs of abuse?
•Is cannabis use associated with disruptions in the amygdala and
habenula as seen with other drugs of abuse?
Funding The project was supported by NIH Intramural Research
Program of the National Institute on Alcoholism and Alcohol Abuse,
Z01AA3009 (AZ, JB, CKL, PM, CW, NDV, GJW).
Compliance with Ethical Standards
Conflicts of Interest The authors report no biomedical financial interests
or potential conflicts of interest.
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give appro-
priate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
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