Abstract
Purpose of Review:
Marijuana (MJ) is one of the most commonly used drugs among adolescents. Exposure to MJ during adolescence can lead to alterations in brain development, and, subsequently to the behavioral correlates regulated by the affected brain regions. In this review, we discuss findings from preclinical and human studies examining the relationship between adolescent MJ use and the neurobiological and behavioral correlates associated with it.
Recent Findings:
Current findings indicate that adolescent MJ use is associated with alterations in brain structure and function, especially in regions that express high levels of the cannabinoid 1 receptor such as the prefrontal cortex, hippocampus, cerebellum and limbic regions. These alterations are correlated with changes in affective, cognitive and reward-seeking behavior. Furthermore, evidence suggests that exposure to MJ during adolescence can have long-lasting and pronounced neural and behavioral effects into adulthood.
Summary:
The wide ranging neural and behavioral correlates associated with MJ use during adolescence highlight the need for further studies to better understand the potential risk factors and/or neurotoxic effects of adolescent MJ use.
Keywords: Marijuana, Adolescence, Neurodevelopment, Behavior, Cognitive, Affective
Introduction
Marijuana (MJ) is one of the most commonly used illicit drugs in the general population and among adolescents. According to the 2019 National Survey on Drug Use and Health (NSDUH), in 2018, an estimated 43.5 million people reported current MJ use, out of which 14.9 million were adolescents and young adults between the ages of 12 – 25 years [1]. In addition, reports from the 2018 Monitoring the Future (MTF) study indicated that approximately 1 in 17 high school seniors use MJ on a daily basis [2]. Recent changes in policy surrounding the legalization of MJ for medical and/or recreational purposes in the United States have led to a lower perception of risk and increased acceptance of MJ use, especially among the adolescent population [3, 4]. Additionally, investigations into the impact of legalization of MJ use among adolescents have demonstrated an increase in reports of MJ abuse or dependence as well as higher incidences of MJ-related emergency department and urgent care visits which have resulted in increased behavioral health evaluations. [3, 5]. These findings suggest greater MJ related adverse consequences following legalization. Furthermore, MJ use during adolescence has been linked to alterations in brain development [6], cognitive dysfunctions [7], increased risk of unhealthy and dangerous behaviors [8], and poorer educational outcomes [9]. These negative consequences are more pronounced with earlier onset of MJ use and can lead to long-lasting detrimental effects in adulthood [10, 11]. This highlights an increased need to further evaluate and understand the potential risk factors and consequences of MJ use in the adolescent population.
Adolescence is a critical developmental period that is characterized by significant changes in physiological, psychological, behavioral and socio-environmental domains [12]. A critical component of adolescent development is the substantial restructuring, reorganization and refinement of brain morphology, chemistry and function that is unique to this developmental period [13]. These neurobiological changes are accompanied by the enhancement and increased efficiency of vital cognitive functions such as attention, inhibitory control and working memory [14]. Furthermore, adolescents exhibit increased sensation-seeking, risk-taking, and reward-driven behaviors, which increases the probability of engaging in harmful activities that can have long-lasting effects [15, 16]. Adolescents also experience a shift in their social environment whereby they start to exert independence from parental supervision and begin to establish strong peer connections that can influence their decision-making processes and social behavior [17]. While all of these changes are important in cultivating a healthy trajectory towards independent, goal-directed actions into adulthood, adolescence is also a period of heightened vulnerability and susceptibility to a wide range of psychopathology including drug-seeking behaviors, conduct disorders, impaired regulation of emotional behaviors and cognitive dysfunction [18, 19].
In this review, we focus on examining the relationships among adolescent MJ use, brain maturation, and behavior. We first describe typical neurodevelopmental processes that are observed during adolescence and the role of the endocannabinoid system (ECS) in regulating these processes. Secondly, we provide a summary of preclinical and human research investigating the impact of MJ use during adolescence on brain development and behavior, with a focus on recent studies from the past 5 years. Finally, we discuss the implications of current findings and potential future directions in this field of study.
Adolescent Brain Development
Longitudinal magnetic resonance imaging (MRI) studies have shown that the adolescent brain undergoes dynamic remodeling in terms of structure and function in order to support the regulation of cognitive and affective functions and its related behavioral outcomes. From an anatomical standpoint, linear increases in white matter volume and nonlinear changes in gray matter volume and cortical thickness have been observed spanning childhood and adolescence [20–22]. Changes in gray matter has been shown to follow an inverted U-shape curve with an increase in gray matter observed during childhood followed by a decrease starting around puberty [23]. These changes follow a specific spatiotemporal pattern. For instance, motor, sensory and limbic regions of the brain that are involved in basic somatosensory functions have been shown to mature earlier followed by associative regions such as the parietal lobe. Frontal and temporal regions of the brain that are involved in higher order cognitive and executive functions, as well as the integration of multiple processes mediated by different brain regions, mature last [20, 24].
Furthermore, structural changes in white matter volume and integrity have been shown to reach their peak during late adolescence into early adulthood and continue to be enhanced throughout the lifespan [25, 26]. This has been demonstrated using diffusion tensor imaging (DTI) studies using measures of fractional anisotropy (FA), mean diffusivity (MD) and radial diffusivity (RD), all indicators of white matter tract integrity and efficiency. Specifically, it has been shown that maturation and increased efficiency of WM tracts are associated with increasing FA and decreasing MD and RD values [27].
Animal and postmortem human studies have provided insight into the potential microstructural changes that underlie the gross structural alterations that are observed in the brain during adolescence [28–30]. It has been reported that decreases in gray matter volume are caused by increased synaptic pruning whereby synapses that are considered unnecessary for a neural pathway are eliminated [31, 32]. Likewise, increases in white matter volume are thought to reflect increased myelination of white matter tracts which increases the efficiency of neural communication and circuitry between different brain regions [27, 33]. In addition, changes in glial cells, vascular pathways and axonal caliber have also been implicated as contributing factors towards the neurodevelopmental processes that take place during adolescence [25, 34].
Along with neuroanatomical changes, the adolescent brain also undergoes significant changes in connectivity between different brain regions, which appear to parallel the pattern of structural development described above. Resting-state functional MRI (fMRI) studies exploring differences in intrinsic functional connectivity networks in the brain from childhood to adulthood demonstrate significant transformations in the distribution, strength and relationship of these networks [35]. Specifically, during childhood, brain networks appear to be more locally distributed due to the short-range connections that exists between regions. As the brain continues to mature, long-range connections are established throughout the brain leading to a more integrated stream of information processing associated with more focal patterns of activation and greater network efficiency [36–38]. Studies of brain maturation have shown the highest level of connectivity in the sensorimotor network in early childhood. In contrast, the association-related network involving the frontal and parietal regions showed greater levels of connectivity which matured in late childhood [39]. In addition, the strength and efficiency of networks are also shaped by environmental and genetic cues that are present during these critical periods of development [40–42].
As development progresses, functional networks in the brain become increasingly specialized and are organized in a hierarchical fashion (e.g. bottom-up vs. top-down processing) [43]. Early adolescence is characterized by bottom-up processing mechanisms that are controlled by the limbic system which includes regions of the brain such as the amygdala and nucleus accumbens. The limbic system plays an important role in regulating emotion, arousal and motivation as well as reward related behaviors [44]. Therefore, early adolescence is associated with heightened emotional reactivity, sensation-seeking behaviors and increased impulsivity [44, 45]. During adolescence, continued development of the PFC increases the efficiency of top-down control mechanisms associated with increased cognitive control and inhibitory capacity [46–49]. It has been hypothesized that increased impulsivity, risk-taking and sensation-seeking during adolescence is due to the different developmental timelines and degree of connectivity between limbic and executive control systems [44, 48, 50].
The anatomical and functional changes observed in adolescent brains are also accompanied by developmental changes in various neurotransmitter systems and the interaction between these systems. Alterations have been observed in monoaminergic neurotransmitters such as dopamine, serotonin and epinephrine which play a key role in regulating reward, motivation and emotional behaviors during adolescence [51, 52]. In addition, critical shifts in the excitatory and inhibitory balance of the brain are occurring during this time [53]. These shifts are mediated by distinct developmental timelines of glutamate and gamma-amino butyric acid (GABA), which are the primary excitatory and inhibitory neurotransmitters in the brain [54]. Specifically, it has been observed that at the onset of adolescence, development of the glutamatergic system is much more advanced compared to the GABAergic system [13]. Hence, imbalances in excitatory and inhibitory control mechanisms have been suggested to be a factor contributing to increased excitatory and impulsive behavior in early adolescence, which further contributes to the vulnerability for increased maladaptive behaviors, such as MJ use [13].
The Endocannabinoid System
Delta-9 tetrahydrocannabinol (THC), the key psychoactive component in MJ, exerts its effects on the brain by disrupting endogenous signaling mechanisms that are mediated by the endocannabinoid system (ECS). This system has been shown to play an important role in neurodevelopmental processes including shaping patterns of brain growth [55]. The ECS is comprised of cannabinoid receptors, CB1 and CB2, endogenous cannabinoid (endocannabinoid) ligands and enzymes that are involved in the synthesis and breakdown of endocannabinoids [56]. The CB1 receptor is one of the most abundantly expressed G-protein coupled receptors in the brain and high levels of CB1 receptor expression have been found in the cerebellum, hippocampus, striatal regions, and prefrontal cortex (PFC) among other regions. These regions are involved in a range of functions including emotion regulation, cognition and reward processing [57–60]. One of the primary functions of the ECS is to regulate the release of neurotransmitters. Endocannabinoids, namely anandamide (N-arachidonoylethanolamide, AEA) and 2-arachidonoyl glycerol (2-AG), are synthesized postsynaptically when required and act as retrograde synaptic messengers on cannabinoid receptors located on presynaptic neurons. This leads to the inhibition of release of various neurotransmitters including glutamate and GABA [55]. Studies have demonstrated that regulation of these neurotransmitters are critical in determining patterns of structural and functional development of the brain and optimal functioning of cognitive processes, especially those involving prefrontal regions [61].
The ECS has been shown to play an integral role in brain development starting from embryonic stages through adulthood by regulating mechanisms that are important for neurogenesis, neuronal cell proliferation, migration and differentiation as well as synaptogenesis and synaptic plasticity [62, 63]. During adolescence, the ECS undergoes dynamic spatiotemporal changes in cannabinoid receptor expression and endocannabinoid levels and it has been shown that CB1 receptor expression as well as AEA and 2-AG levels along with their functionality reaches peak levels during adolescence [64]. These changes are accompanied by complementary changes in enzymes that are involved in the synthesis and breakdown of endocannabinoids leading to heightened ECS activity during this period of development [65]. Although research examining the mechanisms by which the ECS regulates adolescent brain development is still in its infancy, emerging evidence has suggested that during adolescence, the ECS plays a critical role in brain development by facilitating the strengthening and elimination of synapses associated with enhanced information processing [61]. Given the role of the ECS in maintaining homeostatic balance between excitatory and inhibitory signaling via modulation of synaptic activity, this can have important implications as elimination of synapses or synaptic pruning has been indicated as a mechanism that underlies the maturation of cortical regions as described in the previous section. Additionally, in a recent review, it was postulated that endocannabinoid signaling is critical for the development of corticolimbic circuitry involving regions such as the hippocampus, amygdala and PFC which helps regulate stress and emotional behaviors such as fear and anxiety that undergo significant developmental changes during adolescence [65]. This further highlights the importance of the ECS in regulating critical aspects of adolescent development across various regions of the brain which helps shape the maturation of functional circuits that inform a wide range of behaviors which can be perturbed by MJ use.
Neurobiological and Behavioral Correlates Associated with Adolescent MJ Use
As alluded to earlier, adolescence is a time during which critical neurodevelopmental and behavioral changes occur. Understanding normative patterns of adolescent brain development, behavioral manifestations, and the potential mechanisms involved provides us with insights towards how the adolescent brain is susceptible to perturbations caused by different internal and external factors. Preclinical and human studies have demonstrated that disruptions to the ECS related to exogenous compounds, including THC, that occur during adolescence can have significant long-term consequences. These effects can impact multiple levels of development and can be seen in both neurobiological and behavioral changes.
Summary of preclinical studies
Animal models have provided us with the ability to examine potential neurobiological and behavioral mechanisms that are impacted by acute and chronic exposure to exogenous cannabinoids such as THC, especially during adolescence when the brain is undergoing significant transformations. These studies provide insights into the potential neurotoxic effects of THC, specifically in regions of the brain with high CB1 receptor expression levels such as the hippocampus and PFC. For instance, administration of THC in vitro and in vivo has been shown to induce neurotoxic effects on hippocampal neurons leading to reduced synaptic and neuronal density, dendritic length and branching as well as an overall reduction in the volume of neurons [66, 67]. In addition, alterations of CB1 receptor levels have been observed following exposure to THC although the degree of alteration varies depending on the brain region and sex of the animals examined. In one particular study, CB1 mRNA levels were increased in the hippocampus and cerebellum and decreased in the striatum of male rats following 21 days of exposure to THC [68]. Conversely, following exposure to THC for 10 days, CB1 receptors were found to be downregulated in the hippocampus, PFC and ventral midbrain in adolescent female rats [69]. These observations clearly indicate disruptions at a cellular and molecular level that can potentially impact typical neurodevelopmental patterns and behavioral functions that are established during adolescence.
Recent work suggests that exposure to THC or other synthetic CB1 receptor agonists (WIN 55,212–2 or CP-55,940) during adolescence as opposed to adulthood has pronounced and long-lasting cognitive and behavioral effects. For example, chronic THC administration in adolescent male mice was shown to elicit persistent cognitive impairments on an object recognition task, repetitive and compulsive-like behavior demonstrated by increased shredding behavior during nest-building and anxiogenic behavior indicated by decreased exploration of open-arms in the elevated plus maze task [70]. With the exception of anxious behavior, these cognitive and behavioral deficits were not observed in adult mice that were exposed to THC [70]. In a separate study, adult rats exposed to THC during adolescence showed reduced PFC inhibitory function mediated by the GABAergic system [71]. The observed loss of GABAergic function was associated with increased neuronal activity in the medial PFC as well as increased dopaminergic activity in the ventral tegmental area. Additionally, behavioral analysis showed that these THC exposed rats demonstrated short-term memory and social cognitive deficits, lower social motivation, decreased exploratory behaviors and increased anxiety levels compared to control rats. Interestingly, these behavioral impairments as well as the observed increase in dopaminergic activity were reversed following activation of GABA receptors in the medial PFC using pharmacological methods [71]. These recent findings indicate potential neural correlates that are impacted by adolescent exposure to THC that may have significant behavioral and cognitive effects many of which are commonly observed in psychopathology such as schizophrenia.
Adolescent THC exposure has also recently been shown to alter reward-seeking behaviors that can last into adulthood. For instance, chronic stimulation of cannabinoid receptors using WIN 55,212–2 in adolescent male rats led to increased reward-seeking behaviors demonstrated by changes in responses to reward cues as well as increased intake of palatable food in adulthood compared to control rats [72]. Furthermore, in rats that were treated with the cannabinoid agonist, higher levels of the endocannabinoids, AEA and oleoylethanolamide were observed in the nucleus accumbens indicating alterations in ECS function in regions associated with the reward circuitry [72]. This impact on reward circuitry can have important implications with regard to the continued and sustained use of MJ as well as increased addictive behaviors despite the adverse consequences that are associated with it.
Overall, these preclinical studies indicate that exposure to MJ during adolescence can lead to significant alterations at a cellular and molecular level that impacts brain developmental processes. These alterations are associated with a wide-range of changes at a behavioral level that also encompasses cognitive and affective components which can have considerable consequences on overall developmental processes during adolescence.
Summary of human studies
A wide range of studies in humans have investigated the potential risk factors and consequences associated with adolescent MJ use. These studies have focused on areas relating to behavioral attributes such as impulsivity, sensation-seeking and externalizing behaviors [73–75], neurocognitive and neurodevelopmental processes [6, 7], as well as psychopathology [11]. Neuroimaging studies of adolescent MJ users have shown alterations in brain structure, function and neurochemistry. Consistent with preclinical studies, these changes have primarily been observed in regions of the brain that express high levels of CB1 receptors such as the hippocampus, PFC and cerebellum [76]. For example, volumetric analysis has demonstrated smaller hippocampal [77], orbitofrontal cortex (OFC) and medial orbital PFC volumes [78, 79] as well as larger cerebellar volumes [80, 81] in adolescent MJ users compared to controls. However, alterations in brain morphology are not restricted to these specific regions. Studies have also shown changes in volume and cortical thickness in the insular cortex, amygdala, and nucleus accumbens [82, 83].
A number of longitudinal studies have found that adolescent MJ use may impact neurodevelopmental trajectories [84, 85]. Specifically, cortical thickness was found to be higher in all four lobes of the brain over a period of 3 years in heavy marijuana (and alcohol) users compared to controls despite an overall downward trend of cortical thickness measures in both groups [84]. Similarly, Epstein and Kumra (2015) examined cortical thickness in frontal, temporal and parietal regions of adolescent MJ users with and without early onset schizophrenia and healthy adolescents over an 18-month period. The authors found that adolescents with MJ use disorder, regardless of an early onset schizophrenia diagnosis, showed greater cortical thickness in frontal, parietal and temporal regions at the 18-month follow-up compared to non-users. Additionally, reduced cortical thinning in frontal and parietal regions was associated with greater lifetime cannabis use [85]. Although longitudinal studies examining the impact of MJ use on brain maturation during adolescence is limited, current findings provide compelling evidence suggesting potential disruptions in pruning mechanisms and altered brain maturational trajectories associated with adolescent MJ use.
Studies examining white matter alterations using DTI methods have consistently shown decreased FA values and increased MD and RD in adolescent MJ users. These alterations have been observed in WM tracts such as the corpus callosum [86, 87], arcuate fasciculus, which connects fronto-temporal regions [88] and internal capsule, which connects subcortical structures such as caudate putamen to cerebral hemispheres [87] Additionally, earlier age of onset of MJ use was found to be positively correlated with FA values in frontal WM tracts [89] and left and right genu of the corpus callosum [87] indicating altered myelination processes that can lead to underdeveloped and subsequently less efficient WM tracts in MJ users compared to non-users.
Functional MRI techniques have provided us with the ability to understand intrinsic networks of the brain in the absence and presence of active challenges highlighting how these networks can be perturbed by external factors including MJ use. Recent studies examining functional connectivity in adolescent MJ users have demonstrated alterations in neural connectivity that have been associated with a broad array of behavioral processes. For instance, initiation and continued use of illicit substances, including MJ, has been associated with heightened reward seeking behaviors and aberrant reward processing mechanisms, especially in regions of the brain such as the nucleus accumbens, striatum, and ventral tegmental area that are highly regulated by dopamine, a critical neurotransmitter in the reward system [90]. Recent neuroimaging studies examining the relationship between adolescent MJ use and reward functioning have consistently shown alterations in the mesocorticolimbic reward circuitry [91–93]. For example, in a longitudinal study examining the relationship between trajectory of MJ use, connectivity in reward circuitry during a monetary reward task, and psychosocial functioning in high-risk male adolescents, it was found that at 20 years of age, participants with an escalating trajectory of use showed negative coupling between the nucleus accumbens and medial PFC [91]. In contrast, participants who reported consistently high or low MJ use showed positive correlations between these regions. These connectivity patterns were specific to win outcomes of the monetary reward task compared to loss outcomes. Additionally, negative connectivity between the nucleus accumbens and medial PFC at age 20 was associated with higher depression, anhedonia and lower educational attainment 2 years later (22 years of age) [91]. Interestingly, in a recent study using a functional connectomics approach to understand network-level connectivity in MJ-using adolescents and control participants during a monetary incentive delay task, it was found that MJ users demonstrated increased connectivity in reward-related regions of the brain during anticipation of gain conditions which reflected enhanced processing efficiency [92]. The authors suggested that these findings might be due to sensitization of these brain regions to reward related behaviors which could subsequently lead to increased addictive behaviors in MJ users.
Dysfunctions in cognitive control mechanisms such as inhibitory control have also been linked to MJ use during adolescence. For example, in a study examining brain activation patterns during an inhibitory control task—Go/No-Go (GNG), in adolescent MJ users following a 28-day abstinence period, it was found that the MJ-using group showed increased activation in frontal, parietal, and occipital regions of the brain during inhibitory (no-go) trials, and increased activation in the insular, parietal and prefrontal regions during the go trials compared to controls. Interestingly, no significant difference was observed in behavioral performance on the GNG task [94]. Contrastingly, in a separate investigation, adolescent MJ users exhibited a significantly greater number of errors on the inhibitory (No-Go) trials compared to control participants on a GNG task. However, no significant differences were observed in brain activation patterns between both groups [95]. Despite inconsistencies with regard to the behavioral and brain activation patterns which may be due to cohorts studied, methodologies used and differences in task requirements, imaging results suggest that adolescent MJ use is strongly associated with alterations in neural circuitry that is involved in inhibitory function. Changes in frontal inhibitory circuitry could have important implications in terms of development of adaptive goal-directed behaviors that are regulated by critical executive functioning processes such as inhibitory function. Additionally, inhibitory function is linked to traits such as impulsivity and risk-taking behaviors that are associated with MJ use trajectories during adolescence [96, 97].
Alterations in functional connectivity between frontal, temporal, parietal and limbic regions of the brain have been associated with affective outcomes such as depression and anxiety. For instance, using resting-state fMRI, adolescent MJ users showed increased connectivity between the OFC and parietal regions, which was associated with increased depressive symptoms. Furthermore, increased connectivity between the OFC and occipital and temporal regions were correlated with lower anxiety symptoms indicating that differential patterns of activation might underlie affective outcomes related to MJ use during adolescence [98]. In a separate study, increased connectivity between the anterior cingulate cortex and limbic regions were correlated with higher levels of depressive symptoms in young-adult MJ users [99]. In a prospective longitudinal study investigating the relationship between heavy MJ use during adolescence and emotional functioning at a later stage examining neural correlates involved in mediating this relationship, it was found that heavy adolescent MJ users demonstrated greater negative emotionality and lower resiliency at 23 years of age compared to controls. In addition, higher lifetime MJ use was correlated with greater negative emotionality. Activation of the dorsolateral PFC and cuneus/lingual gyrus was found to mediate the relationship between heavy MJ use and later negative emotionality and resiliency, respectively [100]. One recent study also reported an altered relationship between cognitive and affective domains in young adults with MJ dependence. Specifically, in the MJ dependent group, reduced cognitive control was associated with negative emotional components. Additionally, MJ-users demonstrated a stronger link between emotional and cognitive processes compared to controls. The authors suggested that this might lead to increased drug seeking behaviors due to a reduced ability to control reward-driven behaviors especially under a negative emotional state [101]. These findings indicate that MJ use during adolescence is associated with alterations in both cognitive and affective processes key to the regulation of behavior. Furthermore, it has been suggested that disruptions in regulation of affective mechanisms may contribute to deficits in self-monitoring of reward-seeking and motivational behavior that can lead to increased addictive actions including MJ use [102]
The impact of MJ in adolescence has also been informed by studies that have explored the relationship between impulsivity traits and adolescent MJ use using both self-report and behavioral measures. A recent meta-analysis by VanderVeen et al. [73] examining studies using the UPPS-P model of impulsivity and its association with adolescent MJ use found that impulsivity traits related to sensation-seeking, lack of planning, and negative and positive urgency were significantly related to MJ use. Furthermore, with the exception of negative urgency, these traits were also associated with negative consequences linked to MJ use [73]. Likewise, in a study using four different behavioral tasks assessing impulsivity, specifically the Immediate Memory Task (impulse control), GoStop impulsivity paradigm (response inhibition), Two-Choice Impulsivity Paradigm (consequence sensitivity with distinct reward and delay choices) and Single Key Impulsivity Paradigm (consequence sensitivity with free operant choices for rewards), adolescent MJ use was significantly correlated with increased impulsivity determined by measures of response inhibition and consequence sensitivity [103]. Acute increases in impulsivity have also been observed following same day and prior day MJ use compared to days where no MJ use was reported indicating a direct effect of MJ use on impulsivity [104]. These findings emphasize the importance of further understanding the underlying factors that drive the relationship between MJ use and cognitive and behavioral outcomes.
In summary, findings from recent studies indicate that MJ use during adolescence is associated with alterations in neural structure and function, specifically in regions of the brain that are regulated by the ECS. This is consistent with findings from preclinical studies indicating that adolescent MJ use can potentially interfere with critical neurodevelopmental mechanisms that occur during this period. Furthermore, these neural alterations are associated with changes in a wide range of behavioral processes related to cognitive, affective and reward functioning that are important for a healthy transition into adulthood.
Future Directions
With recent changes in legalization and availability of MJ use for medical and recreational purposes, there is an increasing need to better understand the impact of MJ use on adolescent brain and behavior. While current research has provided significant insights into the potential neurobiological and behavioral correlates that are linked to adolescent MJ use, a majority of studies have been cross-sectional in design which limits the ability to determine the direction of causality between MJ use and alterations in brain and behavior. Therefore, it is important to implement more longitudinal study designs to clarify the mechanisms that underlie the relationship between brain development, behavior and MJ use during adolescence. Longitudinal studies will also provide a better understanding regarding the risk factors contributing to initiation of MJ use as well as help clarify potential neurotoxic and behavioral consequences that might result from use. Another important area of consideration is examining the differential impact of MJ on males and females. A number of studies have demonstrated sex differences in neurodevelopmental processes with females demonstrating earlier brain maturational trajectories compared to males [24, 105].Therefore, MJ use during adolescence might differentially perturb these maturational processes and related behaviors in males and females. Studies have also shown that MJ use is often comorbid with other substance use, such as tobacco and alcohol, which have also been shown to be associated with alterations in neural mechanisms and behavior [106, 107]. Therefore, future studies will need to focus on the impact of MJ use in the context of comorbid substance use.
Conclusion
The neurodevelopmental, cognitive and emotional changes observed during adolescence are governed by complex spatiotemporal patterns and mechanisms that appear vulnerable to MJ exposure. Both preclinical and human studies have demonstrated that MJ use during this maturational period is strongly linked to alterations in brain structure and function, especially in regions of the brain such as the PFC which is continuing to mature during this period. Preclinical studies suggest that exogenous cannabinoids such as THC disrupt the endogenous signaling mechanisms mediated by the endocannabinoid system which plays an important role in regulating key mechanisms that underlie brain changes that occur during adolescence. Alterations at the cellular and molecular level impacting brain development have been associated with a wide-range of changes in the central nervous system and at a behavioral level in both animal models and human studies. Human studies reviewed suggest adolescent exposure to MJ may have significant consequences on brain developmental processes including disruptions in pruning mechanisms and compromised development of efficient brain connectivity. Brain regions and networks related to inhibitory functions, affect regulation and reward circuitry have been shown to be impacted by THC exposure. Understanding the relationship between brain-behavior mechanisms altered by MJ use during adolescence will inform development of targeted prevention strategies aimed at reducing the risk of MJ use during this critical period of development. It will also optimize treatment options that can help mitigate the long-term negative consequences related to brain development and behavioral outcomes.
Acknowledgements
DYT and PS are supported by NIDA U01 DA041134. DYT is also supported by the Medical Research Service of the Veteran Affairs Salt Lake City Health Care System, the Department of Veteran Affairs Rocky Mountain Network Mental Illness Research, Education, and Clinical Center (MIRECC). The views in this paper are those of the authors and do not necessarily represent the official policy or position of the Department of Veteran Affairs or the United States Government
Footnotes
Conflict of Interest
DYT and PS declare no conflicts of interest.
Compliance with Ethics Guidelines
Human and Animal Rights:
All reported studies/experiments with human or animal subjects performed by the authors have been previously published and complied with all applicable ethical standards (including the Helsinki declaration and its amendments, institutional/national research committee standards, and international/national/institutional guidelines).
Publisher's Disclaimer: This Author Accepted Manuscript is a PDF file of a an unedited peer-reviewed manuscript that has been accepted for publication but has not been copyedited or corrected. The official version of record that is published in the journal is kept up to date and so may therefore differ from this version.
References
Papers of particular interest, published recently, have been highlighted as:
* Of importance
** Of major importance
- 1.SAMHSA. Key substance use and mental health indicators in the United States: Results from the 2018 National Survey on Drug Use and Health (HHS Publication No. PEP19–5068, NSDUH Series H-54). https://www.samhsa.gov/data/: Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration; 2019. [Google Scholar]
- 2.Miech RA, Johnston LD, Bachman JG, O’Malley PM, Schulenberg JE. Monitoring the Future: A Continuing Study of American Youth (12th-Grade Survey), 2018. Inter-university Consortium for Political and Social Research [distributor]; 2019. [Google Scholar]
- 3.Schuermeyer J, Salomonsen-Sautel S, Price RK, Balan S, Thurstone C, Min SJ et al. Temporal trends in marijuana attitudes, availability and use in Colorado compared to non-medical marijuana states: 2003–11. Drug Alcohol Depend. 2014;140:145–55. doi: 10.1016/j.drugalcdep.2014.04.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Wen H, Hockenberry JM, Druss BG. The Effect of Medical Marijuana Laws on Marijuana-Related Attitude and Perception Among US Adolescents and Young Adults. Prev Sci. 2019;20(2):215–23. doi: 10.1007/s11121-018-0903-8. [DOI] [PubMed] [Google Scholar]
- 5.Wang GS, Davies SD, Halmo LS, Sass A, Mistry RD. Impact of Marijuana Legalization in Colorado on Adolescent Emergency and Urgent Care Visits. J Adolesc Health. 2018;63(2):239–41. doi: 10.1016/j.jadohealth.2017.12.010. [DOI] [PubMed] [Google Scholar]
- 6.Lubman DI, Cheetham A, Yucel M. Cannabis and adolescent brain development. Pharmacol Ther. 2015;148:1–16. doi: 10.1016/j.pharmthera.2014.11.009. [DOI] [PubMed] [Google Scholar]
- 7.Castellanos-Ryan N, Pingault JB, Parent S, Vitaro F, Tremblay RE, Seguin JR. Adolescent cannabis use, change in neurocognitive function, and high-school graduation: A longitudinal study from early adolescence to young adulthood. Dev Psychopathol. 2017;29(4):1253–66. doi: 10.1017/S0954579416001280. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Scholes-Balog KE, Hemphill SA, Evans-Whipp TJ, Toumbourou JW, Patton GC. Developmental trajectories of adolescent cannabis use and their relationship to young adult social and behavioural adjustment: A longitudinal study of Australian youth. Addict Behav. 2016;53:11–8. doi: 10.1016/j.addbeh.2015.09.008. [DOI] [PubMed] [Google Scholar]
- 9.Cobb-Clark DA, Kassenboehmer SC, Le T, McVicar D, Zhang R. ‘High’-School: The Relationship between Early Marijuana Use and Educational Outcomes. Economic Record. 2015;91(293):247–66. doi: 10.1111/1475-4932.12166. [DOI] [Google Scholar]
- 10.Fergusson DM, Boden JM. Cannabis use and later life outcomes. Addiction.2008;103(6):969–76; discussion 77–8. doi: 10.1111/j.1360-0443.2008.02221.x. [DOI] [PubMed] [Google Scholar]
- 11.Volkow ND, Baler RD, Compton WM, Weiss SR. Adverse health effects of marijuana use. N Engl J Med. 2014;370(23):2219–27. doi: 10.1056/NEJMra1402309. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Christie D, Viner R. Adolescent development. BMJ. 2005;330(7486):301–4. doi: 10.1136/bmj.330.7486.301. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Arain M, Haque M, Johal L, Mathur P, Nel W, Rais A et al. Maturation of the adolescent brain. Neuropsychiatr Dis Treat. 2013;9:449–61. doi: 10.2147/NDT.S39776. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Blakemore SJ, Choudhury S. Development of the adolescent brain: implications for executive function and social cognition. J Child Psychol Psychiatry. 2006;47(3–4):296–312. doi: 10.1111/j.1469-7610.2006.01611.x. [DOI] [PubMed] [Google Scholar]
- 15.Steinberg L A Social Neuroscience Perspective on Adolescent Risk-Taking. Dev Rev. 2008;28(1):78–106. doi: 10.1016/j.dr.2007.08.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Romer D Adolescent risk taking, impulsivity, and brain development: implications for prevention. Dev Psychobiol. 2010;52(3):263–76. doi: 10.1002/dev.20442. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Choudhury S, Blakemore SJ, Charman T. Social cognitive development during adolescence. Soc Cogn Affect Neurosci. 2006;1(3):165–74. doi: 10.1093/scan/nsl024. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Dahl RE. Adolescent brain development: a period of vulnerabilities and opportunities. Keynote address. Ann N Y Acad Sci. 2004;1021:1–22. doi: 10.1196/annals.1308.001. [DOI] [PubMed] [Google Scholar]
- 19.Churchwell JC, Yurgelun-Todd DA. Neuroimaging, Adolescence, and Risky Behavior. In: Bardo MT, Fishbein DH, Milich R, editors. Inhibitory Control and Drug Abuse Prevention: From Research to Translation. New York, NY: Springer New York; 2011. p. 101–22. [Google Scholar]
- 20.Giedd JN, Blumenthal J, Jeffries NO, Castellanos FX, Liu H, Zijdenbos A et al. Brain development during childhood and adolescence: a longitudinal MRI study. Nat Neurosci. 1999;2(10):861–3. doi: 10.1038/13158. [DOI] [PubMed] [Google Scholar]
- 21.Tamnes CK, Ostby Y, Fjell AM, Westlye LT, Due-Tonnessen P, Walhovd KB. Brain maturation in adolescence and young adulthood: regional age-related changes in cortical thickness and white matter volume and microstructure. Cereb Cortex. 2010;20(3):534–48. doi: 10.1093/cercor/bhp118. [DOI] [PubMed] [Google Scholar]
- 22.Sowell ER, Thompson PM, Leonard CM, Welcome SE, Kan E, Toga AW. Longitudinal mapping of cortical thickness and brain growth in normal children. J Neurosci. 2004;24(38):8223–31. doi: 10.1523/JNEUROSCI.1798-04.2004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Giedd JN, Lenroot RK, Shaw P, Lalonde F, Celano M, White S et al. Trajectories of anatomic brain development as a phenotype. Novartis Found Symp. 2008;289:101–12; discussion 12–8, 93–5. doi: 10.1002/9780470751251.ch9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Gogtay N, Giedd JN, Lusk L, Hayashi KM, Greenstein D, Vaituzis AC et al. Dynamic mapping of human cortical development during childhood through early adulthood. Proc Natl Acad Sci U S A. 2004;101(21):8174–9. doi: 10.1073/pnas.0402680101. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Lebel C, Deoni S. The development of brain white matter microstructure. Neuroimage. 2018;182:207–18. doi: 10.1016/j.neuroimage.2017.12.097. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Lebel C, Beaulieu C. Longitudinal development of human brain wiring continues from childhood into adulthood. J Neurosci. 2011;31(30):10937–47. doi: 10.1523/JNEUROSCI.5302-10.2011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Lebel C, Gee M, Camicioli R, Wieler M, Martin W, Beaulieu C. Diffusion tensor imaging of white matter tract evolution over the lifespan. Neuroimage. 2012;60(1):340–52. doi: 10.1016/j.neuroimage.2011.11.094. [DOI] [PubMed] [Google Scholar]
- 28.Huttenlocher PR, Dabholkar AS. Regional differences in synaptogenesis in human cerebral cortex. J Comp Neurol. 1997;387(2):167–78. doi:. [DOI] [PubMed] [Google Scholar]
- 29.Zecevic N, Rakic P. Synaptogenesis in monkey somatosensory cortex. Cereb Cortex. 1991;1(6):510–23. doi: 10.1093/cercor/1.6.510. [DOI] [PubMed] [Google Scholar]
- 30.Bourgeois JP, Rakic P. Changes of synaptic density in the primary visual cortex of the macaque monkey from fetal to adult stage. J Neurosci. 1993;13(7):2801–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Huttenlocher PR. Synaptic density in human frontal cortex - developmental changes and effects of aging. Brain Res. 1979;163(2):195–205. doi: 10.1016/0006-8993(79)90349-4. [DOI] [PubMed] [Google Scholar]
- 32.Huttenlocher PR, de Courten C. The development of synapses in striate cortex of man. Hum Neurobiol. 1987;6(1):1–9. [PubMed] [Google Scholar]
- 33.Paus T, Collins DL, Evans AC, Leonard G, Pike B, Zijdenbos A. Maturation of white matter in the human brain: a review of magnetic resonance studies. Brain Res Bull. 2001;54(3):255–66. doi: 10.1016/s0361-9230(00)00434-2. [DOI] [PubMed] [Google Scholar]
- 34.Morrison JH, Hof PR. Life and death of neurons in the aging brain. Science. 1997;278(5337):412–9. doi: 10.1126/science.278.5337.412. [DOI] [PubMed] [Google Scholar]
- 35.Ernst M, Torrisi S, Balderston N, Grillon C, Hale EA. fMRI functional connectivity applied to adolescent neurodevelopment. Annu Rev Clin Psychol. 2015;11:361–77. doi: 10.1146/annurev-clinpsy-032814-112753. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Fair DA, Cohen AL, Power JD, Dosenbach NU, Church JA, Miezin FM et al. Functional brain networks develop from a “local to distributed” organization. PLoS Comput Biol. 2009;5(5):e1000381. doi: 10.1371/journal.pcbi.1000381. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Fair DA, Dosenbach NU, Church JA, Cohen AL, Brahmbhatt S, Miezin FM et al. Development of distinct control networks through segregation and integration. Proc Natl Acad Sci U S A. 2007;104(33):13507–12. doi: 10.1073/pnas.0705843104. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Dosenbach NU, Nardos B, Cohen AL, Fair DA, Power JD, Church JA et al. Prediction of individual brain maturity using fMRI. Science. 2010;329(5997):1358–61. doi: 10.1126/science.1194144. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Khundrakpam BS, Reid A, Brauer J, Carbonell F, Lewis J, Ameis S et al. Developmental changes in organization of structural brain networks. Cereb Cortex. 2013;23(9):2072–85. doi: 10.1093/cercor/bhs187. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Tost H, Bilek E, Meyer-Lindenberg A. Brain connectivity in psychiatric imaging genetics. Neuroimage. 2012;62(4):2250–60. doi: 10.1016/j.neuroimage.2011.11.007. [DOI] [PubMed] [Google Scholar]
- 41.Thompson PM, Ge T, Glahn DC, Jahanshad N, Nichols TE. Genetics of the connectome. Neuroimage. 2013;80:475–88. doi: 10.1016/j.neuroimage.2013.05.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Yang Z, Zuo XN, McMahon KL, Craddock RC, Kelly C, de Zubicaray GI et al. Genetic and Environmental Contributions to Functional Connectivity Architecture of the Human Brain. Cereb Cortex. 2016;26(5):2341–52. doi: 10.1093/cercor/bhw027. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Rubia K Functional brain imaging across development. Eur Child Adolesc Psychiatry. 2013;22(12):719–31. doi: 10.1007/s00787-012-0291-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Casey BJ, Jones RM, Hare TA. The adolescent brain. Ann N Y Acad Sci. 2008;1124:111–26. doi: 10.1196/annals.1440.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Geier CF. Adolescent cognitive control and reward processing: implications for risk taking and substance use. Horm Behav. 2013;64(2):333–42. doi: 10.1016/j.yhbeh.2013.02.008. [DOI] [PubMed] [Google Scholar]
- 46.Luna B, Thulborn KR, Munoz DP, Merriam EP, Garver KE, Minshew NJ et al. Maturation of widely distributed brain function subserves cognitive development. Neuroimage. 2001;13(5):786–93. doi: 10.1006/nimg.2000.0743. [DOI] [PubMed] [Google Scholar]
- 47.Luna B, Sweeney JA. The emergence of collaborative brain function: FMRI studies of the development of response inhibition. Ann N Y Acad Sci. 2004;1021:296–309. doi: 10.1196/annals.1308.035. [DOI] [PubMed] [Google Scholar]
- 48.Steinberg L Cognitive and affective development in adolescence. Trends Cogn Sci. 2005;9(2):69–74. doi: 10.1016/j.tics.2004.12.005. [DOI] [PubMed] [Google Scholar]
- 49.Yurgelun-Todd D Emotional and cognitive changes during adolescence. Curr Opin Neurobiol. 2007;17(2):251–7. doi: 10.1016/j.conb.2007.03.009. [DOI] [PubMed] [Google Scholar]
- 50.Spear LP. The adolescent brain and age-related behavioral manifestations. Neurosci Biobehav Rev. 2000;24(4):417–63. doi: 10.1016/s0149-7634(00)00014-2. [DOI] [PubMed] [Google Scholar]
- 51.Wahlstrom D, Collins P, White T, Luciana M. Developmental changes in dopamine neurotransmission in adolescence: behavioral implications and issues in assessment. Brain Cogn. 2010;72(1):146–59. doi: 10.1016/j.bandc.2009.10.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Crews F, He J, Hodge C. Adolescent cortical development: a critical period of vulnerability for addiction. Pharmacol Biochem Behav. 2007;86(2):189–99. doi: 10.1016/j.pbb.2006.12.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Renard J, Rushlow WJ, Laviolette SR. Effects of Adolescent THC Exposure on the Prefrontal GABAergic System: Implications for Schizophrenia-Related Psychopathology. Front Psychiatry. 2018;9:281. doi: 10.3389/fpsyt.2018.00281. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Behuet S, Cremer JN, Cremer M, Palomero-Gallagher N, Zilles K, Amunts K. Developmental Changes of Glutamate and GABA Receptor Densities in Wistar Rats. Front Neuroanat. 2019;13:100. doi: 10.3389/fnana.2019.00100. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Mechoulam R, Parker LA. The endocannabinoid system and the brain. Annu Rev Psychol. 2013;64:21–47. doi: 10.1146/annurev-psych-113011-143739. [DOI] [PubMed] [Google Scholar]
- 56.Lu HC, Mackie K. An Introduction to the Endogenous Cannabinoid System. Biol Psychiatry. 2016;79(7):516–25. doi: 10.1016/j.biopsych.2015.07.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Herkenham M, Lynn AB, Johnson MR, Melvin LS, de Costa BR, Rice KC. Characterization and localization of cannabinoid receptors in rat brain: a quantitative in vitro autoradiographic study. J Neurosci. 1991;11(2):563–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Moldrich G, Wenger T. Localization of the CB1 cannabinoid receptor in the rat brain. An immunohistochemical study. Peptides. 2000;21(11):1735–42. doi: 10.1016/s0196-9781(00)00324-7. [DOI] [PubMed] [Google Scholar]
- 59.Mackie K Distribution of cannabinoid receptors in the central and peripheral nervous system. Handb Exp Pharmacol. 2005(168):299–325. doi: 10.1007/3-540-26573-2_10. [DOI] [PubMed] [Google Scholar]
- 60.Alger BE. Getting high on the endocannabinoid system. Cerebrum. 2013;2013:14. [PMC free article] [PubMed] [Google Scholar]
- 61.Bossong MG, Niesink RJ. Adolescent brain maturation, the endogenous cannabinoid system and the neurobiology of cannabis-induced schizophrenia. Prog Neurobiol. 2010;92(3):370–85. doi: 10.1016/j.pneurobio.2010.06.010. [DOI] [PubMed] [Google Scholar]
- 62.Katona I, Freund TF. Multiple functions of endocannabinoid signaling in the brain. Annu Rev Neurosci. 2012;35:529–58. doi: 10.1146/annurev-neuro-062111-150420. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63.Castillo PE, Younts TJ, Chavez AE, Hashimotodani Y. Endocannabinoid signaling and synaptic function. Neuron. 2012;76(1):70–81. doi: 10.1016/j.neuron.2012.09.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 64.Heng L, Beverley JA, Steiner H, Tseng KY. Differential developmental trajectories for CB1 cannabinoid receptor expression in limbic/associative and sensorimotor cortical areas. Synapse. 2011;65(4):278–86. doi: 10.1002/syn.20844. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 65.Meyer HC, Lee FS, Gee DG. The Role of the Endocannabinoid System and Genetic Variation in Adolescent Brain Development. Neuropsychopharmacology. 2018;43(1):21–33. doi: 10.1038/npp.2017.143. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 66.Rubino T, Realini N, Braida D, Guidi S, Capurro V, Vigano D et al. Changes in hippocampal morphology and neuroplasticity induced by adolescent THC treatment are associated with cognitive impairment in adulthood. Hippocampus. 2009;19(8):763–72. doi: 10.1002/hipo.20554. [DOI] [PubMed] [Google Scholar]
- 67.Chan GC, Hinds TR, Impey S, Storm DR. Hippocampal neurotoxicity of Delta9-tetrahydrocannabinol. J Neurosci. 1998;18(14):5322–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 68.Zhuang S, Kittler J, Grigorenko EV, Kirby MT, Sim LJ, Hampson RE et al. Effects of long-term exposure to delta9-THC on expression of cannabinoid receptor (CB1) mRNA in different rat brain regions. Brain Res Mol Brain Res. 1998;62(2):141–9. doi: 10.1016/s0169-328x(98)00232-0. [DOI] [PubMed] [Google Scholar]
- 69.Burston JJ, Wiley JL, Craig AA, Selley DE, Sim-Selley LJ. Regional enhancement of cannabinoid CB₁ receptor desensitization in female adolescent rats following repeated Delta-tetrahydrocannabinol exposure. Br J Pharmacol. 2010;161(1):103–12. doi: 10.1111/j.1476-5381.2010.00870.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 70.Murphy M, Mills S, Winstone J, Leishman E, Wager-Miller J, Bradshaw H et al. Chronic Adolescent Delta(9)-Tetrahydrocannabinol Treatment of Male Mice Leads to Long-Term Cognitive and Behavioral Dysfunction, Which Are Prevented by Concurrent Cannabidiol Treatment. Cannabis Cannabinoid Res. 2017;2(1):235–46. doi: 10.1089/can.2017.0034.** This study examined the cognitive and behavioral outcomes in a mice model exposed to THC, CBD and a combination of both during adolescence. Chronic exposure to THC was shown to lead to behavioral and cognitive impairments which were not observed in the CBD or CBD+THC group.
- 71.Renard J, Szkudlarek HJ, Kramar CP, Jobson CEL, Moura K, Rushlow WJ et al. Adolescent THC Exposure Causes Enduring Prefrontal Cortical Disruption of GABAergic Inhibition and Dysregulation of Sub-Cortical Dopamine Function. Sci Rep. 2017;7(1):11420. doi: 10.1038/s41598-017-11645-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 72.Schoch H, Huerta MY, Ruiz CM, Farrell MR, Jung KM, Huang JJ et al. Adolescent cannabinoid exposure effects on natural reward seeking and learning in rats. Psychopharmacology (Berl). 2018;235(1):121–34. doi: 10.1007/s00213-017-4749-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 73.VanderVeen JD, Hershberger AR, Cyders MA. UPPS-P model impulsivity and marijuana use behaviors in adolescents: A meta-analysis. Drug Alcohol Depend. 2016;168:181–90. doi: 10.1016/j.drugalcdep.2016.09.016. [DOI] [PubMed] [Google Scholar]
- 74.Martin CA, Kelly TH, Rayens MK, Brogli BR, Brenzel A, Smith WJ et al. Sensation seeking, puberty, and nicotine, alcohol, and marijuana use in adolescence. J Am Acad Child Adolesc Psychiatry. 2002;41(12):1495–502. doi: 10.1097/00004583-200212000-00022. [DOI] [PubMed] [Google Scholar]
- 75.Hayatbakhsh MR, McGee TR, Bor W, Najman JM, Jamrozik K, Mamun AA. Child and adolescent externalizing behavior and cannabis use disorders in early adulthood: an Australian prospective birth cohort study. Addict Behav. 2008;33(3):422–38. doi: 10.1016/j.addbeh.2007.10.004. [DOI] [PubMed] [Google Scholar]
- 76.Brumback T, Castro N, Jacobus J, Tapert S. Effects of Marijuana Use on Brain Structure and Function: Neuroimaging Findings from a Neurodevelopmental Perspective. Int Rev Neurobiol. 2016;129:33–65. doi: 10.1016/bs.irn.2016.06.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 77.Ashtari M, Avants B, Cyckowski L, Cervellione KL, Roofeh D, Cook P et al. Medial temporal structures and memory functions in adolescents with heavy cannabis use. J Psychiatr Res. 2011;45(8):1055–66. doi: 10.1016/j.jpsychires.2011.01.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 78.Cheetham A, Allen NB, Whittle S, Simmons JG, Yucel M, Lubman DI. Orbitofrontal volumes in early adolescence predict initiation of cannabis use: a 4-year longitudinal and prospective study. Biol Psychiatry. 2012;71(8):684–92. doi: 10.1016/j.biopsych.2011.10.029. [DOI] [PubMed] [Google Scholar]
- 79.Churchwell JC, Lopez-Larson M, Yurgelun-Todd DA. Altered frontal cortical volume and decision making in adolescent cannabis users. Front Psychol. 2010;1:225. doi: 10.3389/fpsyg.2010.00225. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 80.Medina KL, Nagel BJ, Tapert SF. Abnormal cerebellar morphometry in abstinent adolescent marijuana users. Psychiatry Res. 2010;182(2):152–9. doi: 10.1016/j.pscychresns.2009.12.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 81.Cousijn J, Wiers RW, Ridderinkhof KR, van den Brink W, Veltman DJ, Goudriaan AE. Grey matter alterations associated with cannabis use: results of a VBM study in heavy cannabis users and healthy controls. Neuroimage. 2012;59(4):3845–51. doi: 10.1016/j.neuroimage.2011.09.046. [DOI] [PubMed] [Google Scholar]
- 82.Lopez-Larson MP, Bogorodzki P, Rogowska J, McGlade E, King JB, Terry J et al. Altered prefrontal and insular cortical thickness in adolescent marijuana users. Behav Brain Res. 2011;220(1):164–72. doi: 10.1016/j.bbr.2011.02.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 83.Gilman JM, Kuster JK, Lee S, Lee MJ, Kim BW, Makris N et al. Cannabis use is quantitatively associated with nucleus accumbens and amygdala abnormalities in young adult recreational users. J Neurosci. 2014;34(16):5529–38. doi: 10.1523/JNEUROSCI.4745-13.2014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 84.Jacobus J, Squeglia LM, Meruelo AD, Castro N, Brumback T, Giedd JN et al. Cortical thickness in adolescent marijuana and alcohol users: A three-year prospective study from adolescence to young adulthood. Dev Cogn Neurosci. 2015;16:101–9. doi: 10.1016/j.dcn.2015.04.006.* This longitudinal study is one of the first to demonstrate how trajectory of brain maturation during adolescence might be impacted by MJ (and alcohol) use. Furthermore, findings from this study is aligned with preclinical findings which have demonstrated disruptions in mechanisms involved in brain development during adolescence.
- 85.Epstein KA, Kumra S. Altered cortical maturation in adolescent cannabis users with and without schizophrenia. Schizophrenia Research. 2015;162(1):143–52. doi: 10.1016/j.schres.2014.11.029 [DOI] [PubMed] [Google Scholar]
- 86.Arnone D, Barrick TR, Chengappa S, Mackay CE, Clark CA, Abou-Saleh MT. Corpus callosum damage in heavy marijuana use: preliminary evidence from diffusion tensor tractography and tract-based spatial statistics. Neuroimage. 2008;41(3):1067–74. doi: 10.1016/j.neuroimage.2008.02.064. [DOI] [PubMed] [Google Scholar]
- 87.Gruber SA, Dahlgren MK, Sagar KA, Gonenc A, Lukas SE. Worth the wait: effects of age of onset of marijuana use on white matter and impulsivity. Psychopharmacology (Berl). 2014;231(8):1455–65. doi: 10.1007/s00213-013-3326-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 88.Ashtari M, Cervellione K, Cottone J, Ardekani BA, Sevy S, Kumra S. Diffusion abnormalities in adolescents and young adults with a history of heavy cannabis use. J Psychiatr Res. 2009;43(3):189–204. doi: 10.1016/j.jpsychires.2008.12.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 89.Gruber SA, Silveri MM, Dahlgren MK, Yurgelun-Todd D. Why so impulsive? White matter alterations are associated with impulsivity in chronic marijuana smokers. Exp Clin Psychopharmacol. 2011;19(3):231–42. doi: 10.1037/a0023034. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 90.Lupica CR, Riegel AC, Hoffman AF. Marijuana and cannabinoid regulation of brain reward circuits. Br J Pharmacol. 2004;143(2):227–34. doi: 10.1038/sj.bjp.0705931. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 91.Lichenstein SD, Musselman S, Shaw DS, Sitnick S, Forbes EE. Nucleus accumbens functional connectivity at age 20 is associated with trajectory of adolescent cannabis use and predicts psychosocial functioning in young adulthood. Addiction. 2017;112(11):1961–70. doi: 10.1111/add.13882. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 92.Nestor LJ, Behan B, Suckling J, Garavan H. Cannabis-dependent adolescents show differences in global reward-associated network topology: A functional connectomics approach. Addict Biol. 2020;25(2):e12752. doi: 10.1111/adb.12752. [DOI] [PubMed] [Google Scholar]
- 93.Martz ME, Trucco EM, Cope LM, Hardee JE, Jester JM, Zucker RA et al. Association of Marijuana Use With Blunted Nucleus Accumbens Response to Reward Anticipation. JAMA Psychiatry. 2016;73(8):838–44. doi: 10.1001/jamapsychiatry.2016.1161. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 94.Tapert SF, Schweinsburg AD, Drummond SP, Paulus MP, Brown SA, Yang TT et al. Functional MRI of inhibitory processing in abstinent adolescent marijuana users. Psychopharmacology (Berl). 2007;194(2):173–83. doi: 10.1007/s00213-007-0823-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 95.Behan B, Connolly CG, Datwani S, Doucet M, Ivanovic J, Morioka R et al. Response inhibition and elevated parietal-cerebellar correlations in chronic adolescent cannabis users. Neuropharmacology. 2014;84:131–7. doi: 10.1016/j.neuropharm.2013.05.027. [DOI] [PubMed] [Google Scholar]
- 96.de Wit H Impulsivity as a determinant and consequence of drug use: a review of underlying processes. Addict Biol. 2009;14(1):22–31. doi: 10.1111/j.1369-1600.2008.00129.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 97.Quinn PD, Harden KP. Differential changes in impulsivity and sensation seeking and the escalation of substance use from adolescence to early adulthood. Dev Psychopathol. 2013;25(1):223–39. doi: 10.1017/S0954579412000284. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 98.Subramaniam P, Rogowska J, DiMuzio J, Lopez-Larson M, McGlade E, Yurgelun-Todd D. Orbitofrontal connectivity is associated with depression and anxiety in marijuana-using adolescents. J Affect Disord. 2018;239:234–41. doi: 10.1016/j.jad.2018.07.002. [DOI] [PubMed] [Google Scholar]
- 99.Shollenbarger S, Thomas AM, Wade NE, Gruber SA, Tapert SF, Filbey FM et al. Intrinsic Frontolimbic Connectivity and Mood Symptoms in Young Adult Cannabis Users. Front Public Health. 2019;7:311. doi: 10.3389/fpubh.2019.00311. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 100.Heitzeg MM, Cope LM, Martz ME, Hardee JE, Zucker RA. Brain activation to negative stimuli mediates a relationship between adolescent marijuana use and later emotional functioning. Dev Cogn Neurosci. 2015;16:71–83. doi: 10.1016/j.dcn.2015.09.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 101.Manza P, Shokri-Kojori E, Volkow ND. Reduced Segregation Between Cognitive and Emotional Processes in Cannabis Dependence. Cereb Cortex. 2020;30(2):628–39. doi: 10.1093/cercor/bhz113.** This article provides insights into the relationship between cognitive and affective processes in the brain that are associated with MJ use and dependence in a young adult cohort. Findings suggest that negative emotional states might lead to poorer cognitive control which also alters reward-driven behaviors in MJ users.
- 102.Pacheco-Colon I, Limia JM, Gonzalez R. Nonacute effects of cannabis use on motivation and reward sensitivity in humans: A systematic review. Psychol Addict Behav. 2018;32(5):497–507. doi: 10.1037/adb0000380.* This review provides important insights into the relationship between affective behaviors such as motivation and reward-seeking in MJ users. Neuroimaging studies identifying potential neural correlates involved in these processes are discussed.
- 103.Dougherty DM, Mathias CW, Dawes MA, Furr RM, Charles NE, Liguori A et al. Impulsivity, attention, memory, and decision-making among adolescent marijuana users. Psychopharmacology (Berl). 2013;226(2):307–19. doi: 10.1007/s00213-012-2908-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 104.Ansell EB, Laws HB, Roche MJ, Sinha R. Effects of marijuana use on impulsivity and hostility in daily life. Drug Alcohol Depend. 2015;148:136–42. doi: 10.1016/j.drugalcdep.2014.12.029. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 105.Koolschijn PC, Crone EA. Sex differences and structural brain maturation from childhood to early adulthood. Dev Cogn Neurosci. 2013;5:106–18. doi: 10.1016/j.dcn.2013.02.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 106.Subramaniam P, McGlade E, Yurgelun-Todd D. Comorbid Cannabis and Tobacco Use in Adolescents and Adults. Curr Addict Rep. 2016;3(2):182–8. doi: 10.1007/s40429-016-0101-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 107.Lisdahl KM, Gilbart ER, Wright NE, Shollenbarger S. Dare to delay? The impacts of adolescent alcohol and marijuana use onset on cognition, brain structure, and function. Front Psychiatry. 2013;4:53. doi: 10.3389/fpsyt.2013.00053. [DOI] [PMC free article] [PubMed] [Google Scholar]
