The Neuroscience of Addiction (Francesca Mapua Filbey) (Z-Library)

/ Liu, P., Lu, H., Filbey, F. M.,et al. (2014). MRI assessment of cerebral oxygen metabolism in cocaine-addicted individuals: hypoactivity and dose dependence. NMR Biomed, 27(6), 726–732. doi:10.1002/nbm.3114 McClure, S. M. & Bickel, W. K. (2014). A dual-systems perspective on addiction: contributions from neuroimaging and cognitive training.Ann N Y Acad Sci, 1327(1), 62–78. doi:10.1111/nyas.12561 Mello, N. K. (1973). A review of methods to induce alcohol addiction in animals. Pharmacol Biochem Behav, 1(1), 89–101. Morgenstern, J., Naqvi, N. H., Debellis, R. & Breiter, H. C. (2013). The contributions of cognitive neuroscience and neuroimaging to understanding mechanisms of behavior change in addiction.Psychol Addict Behav, 27(2), 336–350. doi:10.1037/a0032435 Myers, K. M. & Carlezon, W. A., Jr. (2010). Extinction of drug- and withdrawal-paired cues in animal models: relevance to the treatment of addiction. Neurosci Biobehav Rev, 35(2), 285–302. doi:10.1016/j. neubiorev.2010.01.011 Nader, M. A., Czoty, P. W., Gould, R. W. & Riddick, N. V. (2008). Positron emission tomography imaging studies of dopamine receptors in primate models of addiction. Philos Trans R Soc Lond B Biol Sci, 363(1507), 3223–3232. doi:10.1098/rstb.2008.0092 Parvaz, M. A., Alia-Klein, N., Woicik, P. A., Volkow, N. D. & Goldstein, R. Z. (2011). Neuroimaging for drug addiction and related behaviors.Rev Neurosci, 22(6), 609–624. doi:10.1515/RNS.2011.055 Stapleton, J., West, R., Marsden, J. & Hall, W. (2012). Research methods and statistical techniques in addiction. Addiction, 107(10), 1724–1725. doi:10.1111/j.1360-0443.2012.03969.x Yalachkov, Y., Kaiser, J. & Naumer, M. J. (2012). Functional neuroimaging studies in addiction: multisensory drug stimuli and neural cue reactivity. Neurosci Biobehav Rev, 36(2), 825–835. doi:10.1016/j.neubiorev.2011.12.004 Spotlight 1 Love on the brain Advancements in neuroimaging technology have demonstrated that the brain functions via well-orchestrated, interconnected networks of brain regions. These intrinsically linked brain networks simultaneously activate when we are “at rest” or not performing a specific task. There is growing research in how these “resting-state” networks may be related to individual factors. 30 Human Neuroscience Approaches

/ Research has widely accepted that feelings of love are rewarding and are therefore also subserved by the reward network. It is therefore expected that as our feelings of love changes, so do the brain regions that underlie these processes (Figure S2.1). Recently, a group of researchers examined how changes in feelings of love may influence resting-state networks. They found that functional connectivity (i.e. how temporally synchronized neural responses are between regions) within the reward, motivation and emotion regulation network (dorsal anterior cingulate cortex, insula, caudate, amygdala and nucleus accumbens) was greater in a group of participants who self-reported being “in love” compared with those who were not in love (ended romantic relationship recently/ never been in love). Figure S2.1 What does 45 years of love look like in the brain? Spotlight 1 31

/ Spotlight 2 Can we use neuroimaging to predict future behavior? Imagine if we could predict the later development of mental disorders, including addiction, in children (Figure S2.2). Can information gathered today be used to support the individual in order to prevent (or delay) the potential onset of mental disorders? Current research is capitalizing on neuroimaging techniques in order to make the ability to predict and prevent disorders a reality. Recently, the National Institutes of Health (NIH) funded a historic study called the Adolescent Brain Cognitive Development or ABCD Study (https://abcdstudy.org/) that has the ultimate goal of using advanced brain imaging to map brain development in order tofind predictors of mental health issues and addiction. This nationwide study on 10,000 9–10-year-olds will collect information on mental health, addiction, education, culture, environment and genetics to determine how these factors may be associated with how the brain develops. Children from this study will be tested yearly over a 10-year period to identify risk factors and protective factors, mental health issues and addiction. The ability to predict will ultimately lead to better outcomes for our children. References Bloch, F., Hansen, W. W. & Packard, M. (1946). Nuclear induction.Phys Rev, 69(3–4), 127. doi:10.1103/PhysRev.69.127 Cohen, D. & Cuffin, B. N. (1991). EEG versus MEG localization accuracy: theory and experiment. Brain Topogr, 4(2), 95–103. doi:10.1007/ BF01132766 Figure S2.2 Associating the brain with behavior began with thefield of phrenology. From www.pexels.com/photo/photo-of-head-bust-print-artwork-724994/. 32 Human Neuroscience Approaches

/ Huettel, S. A., Song, A. W. & McCarthy, G. (2008).Functional Magnetic Resonance Imaging, 2nd edn. Sunderland, MA: Sinauer Associates. Ogawa, S., Lee, T. M., Kay, A. R. & Tank, D. W. (1990). Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A, 87(24): 9868–9872. doi:9868–9872. 10.1073/ pnas.87.24.9868 Pagliaccio, D., Barch, D. M., Bogdan, R.,et al. (2015). Shared predisposition in the association between cannabis use and subcortical brain structure. JAMA Psychiatry, 72(10), 994–1001. doi:10.1001/ jamapsychiatry.2015.1054 Purcell, E. M., Torrey, H. C. & Pound, R. V. (1946). Resonance absorption by nuclear moments in a solid. Phys Rev, 69(1–2), 37–38. doi:10.1103/ PhysRev.69.37 Rosenbloom, M. J., Sullivan, E. V. & Pfefferbaum, A. (2010). Focus on the brain: HIV infection and alcoholism: comorbidity effects on brain structure and function. Alcohol Res Health, 33(3), 247–257. Schacht, J. P., Hutchison, K. E. & Filbey, F. M. (2012). Associations between cannabinoid receptor-1 (CNR1) variation and hippocampus and amygdala volumes in heavy cannabis users. Neuropsychopharmacology, 37(11), 2368–2376. doi:10.1038/ npp.2012.92 Whitford, T. J., Savadjiev, P., Kubicki, M.,et al. (2011). Fiber geometry in the corpus callosum in schizophrenia: evidence for transcallosal misconnection. Schizophrenia Res, 132(1), 69–74. doi:10.1016/ j.schres.2011.07.010 References 33

/ CH A P TER TH REE Brain-Behavior Theories of Addiction Learning Objectives • Be able to identify different brain-based models of addiction. • Be able to explain the difference between“wanting” and “liking” in the context of incentive sensitization. • Be able to discuss the process of opponent processes in addiction. • Be able to describe the role of the prefrontal cortex in the behavioral manifestations of addiction as presented by the iRISA syndrome model. • Be able to explain the mechanisms behind the cue-elicited craving model. Introduction The National Institute of Drug Abuse (NIDA) in the USA defines drug addiction as a “chronic, often relapsing brain disease.” In this vein, multiple models of addiction have been proposed to explain the link between brain mechanisms and observable behavioral symptoms of addiction. These conceptual or theoretical models have advanced neuroscience research in addiction by providing a working framework that can be tested and elaborated upon. This chapter will describe some of the predominant models including the incentive-sensitization theory, the allostasis theory, the impaired response inhibition and salience attribution (iRISA) syndrome model and the cue-elicited craving model. Initial theories on substance use assumed that the pleasurable effects of the drug instigate drug consumption, and that dependence develops out of a persistent drive to obtain these positive effects. These initial theories, however, failed to incorporate other aspects that occur during the progression of the disorder, such as tolerance and withdrawal. The idea of withdrawal during addiction suggests a shift in the progression of the disorder from one that is initially driven by positive incentives to one that is negatively reinforced, such as to avoid the withdrawal symptoms following cessation of drug use. Such a shift would suggest neural

/ adaptations during the progression toward addiction. In 1993, Robinson and Berridge proposed an “incentive-sensitization” model in which drugs of abuse cause alterations in a number of neural systems, specifically in areas that control motivation and reward. Koob and Le Moal (1997, 2008) proposed a neurobiological model stemming from motivation theories that described a pathological shift in the “hedonic set point” resulting in a loss of control over drug intake. Prior to the 21st century, most of the neurobiological models focused largely on subcortical processes that do not capture behavioral, cognitive and emotional factors that are also crucial to the development of addiction. Addressing these gaps, emerging theories integrate cortical aspects of drug-induced neuroadaptations, and provide testable hypotheses and contribute unique perspectives of addiction. The Incentive-Sensitization Theory Developed by Robinson and Berridge (1993), this is the first neuroadaptationist model, which suggests that neural changes that occur during repeated substance use impact neural substrates underlying reinforcement and motivation. According to this theory, addiction develops from hypersensitization to the drug’s effects in mesocorticolimbic regions, which leads to the drug’s increased incentive salience. Incentive salience is a reward-based motivational state driven by a strong, subconscious association between the drug and the feelings of reward, thereby resulting in pathological motivation for drugs (compulsive “wanting” as opposed to “liking”). This model proposes that incentive sensitization of drug stimuli stems from changes in memory and learning systems that direct motivation to specific and appropriate stimuli. Specifically, associative learning processes modulate neural sensitization that manifests as behavioral sensitivity in conditioned (previously learned) environments (Anagnostaras et al., 2002). Dopamine-related pathways are implicated in the “wanting” (dopamine and glutamate in corticolimbic regions) aspect of this model, which is different from “liking” (dopaminergic, GABAergic, endocannabinoid and opioid signaling associated with the dorsal striatum). In this manner, drug acquisition “short circuits” the normal relationship between behavior and its resulting hedonic value that would otherwise allow for the encoding of important survival information, such as food consumption and sex. Although limited, potential mechanisms for sensitization have been demonstrated in both pre-clinical and clinical studies. In sensitized animals, increased firing is frequently observed in mesolimbic neurons. The Incentive-Sensitization Theory 35

/ Similar findings have been observed in humans using positron emission tomography (PET) and [ 11C]raclopride. Boileau et al. (2006) reported greater psychomotor response and increased dopamine release (i.e. a greater reduction in [ 11C]raclopride binding) in the ventral striatum in amphetamine-sensitized men and this effect was still present at the 1-year follow-up. The Allostatic Model: Dysregulation in Homeostasis This model was developed to explain the motivational mechanisms that drive excessive drug seeking and loss of control over drug use, and is founded largely on the opponent-process motivation theory of emotions proposed by Solomon and Corbit (1974). The opponent-process theory states that the expression of one emotion (e.g. pleasure) suppresses the opposite emotion (e.g. pain). Specifically, in response to a stimulus, the initial response is of heightened arousal, which is short lived and intense. This positive response is followed by a gradual dip toward the opposite, negative affective response that decays back into normal equilibrium or homeostasis, i.e. a stable state of moderate arousal. Solomon and Corbit (1974) referred to the negative affect component as the opponent process. From an addiction perspective, the opponent-process theory of motivation suggests that the initial pleasurable feelings (euphoria, relief from anxiety) from drug use are followed by the opponent process of negative emotional experiences, such as withdrawal symptoms (e.g. headache, nausea). In other words, the acute hedonic state produced by drug use is opposed by the brain ’s mechanisms to return to homeostasis. This process is complicated by the fact that, with repeated drug use, tolerance develops whereby greater amounts of the drug are needed in order to achieve the same hedonic state. Interestingly, however, according to the opponent-process theory, tolerance is not the result of habituation to the positive effects but rather a sensitization to the negative effects. Thus, the opponent-process theory suggests that repeated drug use leads to larger effects of the opponent process, while the hedonic state becomes smaller. Continued drug use is therefore motivated by the need to avoid these negative states (see Chapter 6). Koob & Le Moal (1997) extended this model to incorporate the neurobiological adaptations that underlie this dysregulation in homeostasis (Figure 3.1). They described three stages of addiction: 1) binge intoxication, followed by 2) the withdrawal/negative affect, and then by 3) preoccupation/anticipation that would be likely to resume the cycle. 36 Brain-Behavior Theories of Addiction

/ Neurobiologically, the sensation of reward during the first phase occurs as a result of excitatory dopaminergic signaling in the nucleus accumbens. This intense pleasure is encoded as a highly salient and rewarding memory. However, while this positive memory may encourage substance seeking, on a cellular level this heightened reward signaling reflects two states of imbalance: within systems, whereby receptors triggered by specific substances are downregulated to maintain homeostasis in the presence of the substance, and between systems, which reflects heightened connectivity between reward regions and decreased connectivity from inhibitory regions such as the prefrontal cortex (PFC) to reward regions. The second stage, withdrawal, is characterized by downregulation of the relevant receptor in an effort to maintain homeostasis in the presence of the substance (e.g. dopamine in the case of cocaine, opioid receptors in the case of heroin, GABA receptors in the case of alcohol). Additionally, in this paradigm, the experience of tolerance reflects the general decrease in excitatory dopaminergic signaling in the substance-adapted state. However, without the substance, the reward circuitry is “underwhelmed,” manifesting as symptoms of Preoccupation with obtaining persistent physical/ psychological problems ADDICTION Persistent desire Larger amounts taken than expected Tolerance withdrawal compromised social, occupational or recreational activities Preoccupation/ anticipation Binge Withdrawal/ negative affect intoxication Figure 3.1 Diagram describing the addiction cycle– preoccupation/anticipation (“craving”), binge/intoxication and withdrawal/negative affect– with the different criteria for substance dependence incorporated from theDiagnostic and Statistical Manual of Mental Disorders, 4th edn. (Adapted from Koob & Le Moal, 2008.) The Allostatic Model: Dysregulation in Homeostasis 37

/ negative affect, physical discomfort and dysphoria. This perpetuates until the individual alleviates this negative state with substance use, which initiates both a new high and a subsequent low. The third stage consists of preoccupation, anticipation or craving. This is characterized by the individual’s drive to avoid discomfort, whereby substances are used in an effort to “feel normal” and to prevent withdrawal symptoms (versus feeling pleasure). This state reflects long-term changes in neural networks that place individuals at high risk for relapse after a period of cessation. The Impaired Response Inhibition and Salience Attribution (iRISA) Syndrome Model In 2002, Goldstein and Volkow proposed one of the first models that integrate the behavioral, cognitive and emotional features in existing models of addiction. Their model, the iRISA syndrome model, is based predominantly on neuroimaging findings in cocaine-using populations and highlights the important role of the PFC neurocircuitry in moderating clusters of interconnected behaviors (Figure 3.2): drug intoxication, drug craving, compulsive drug administration and drug withdrawal. The specific PFC regions include dorsal PFC subregions (the dorsolateral PFC, dorsal anterior cingulate cortex and inferior frontal gyrus) that are involved in higher-order control or “cold” processes. Ventral PFC subregions (the medial orbitofrontal cortex, ventromedial PFC and rostroventral anterior cingulate cortex) are involved in more automatic, emotion-related processes or “hot” processes. The iRISA model proposes that drug intoxication, which is traditionally viewed as the result of neural changes in subcortical regions, is also accompanied by increased dopamine levels in frontal regions as well as activation in the PFC and anterior cingulate gyrus. Furthermore, the patterns of activation are associated with the subjective perception of intoxication, the reinforcing effects of the drug or enhanced mood. Drug craving – a conditioned response to drugs that involves memory processes – is also suggested to be associated with activation in the orbitofrontal and anterior cingulate cortices. Greater activation in these regions has been demonstrated across different substance-abusing populations and via different drug cue modalities (e.g. visual, tactile, gustatory). Similar to intoxication, activation in these prefrontal regions also correlates with self-reports of craving. Compulsive drug administration that occurs during the shift from the hedonic state to the negative state 38 Brain-Behavior Theories of Addiction

/ (similar to the opponent process described earlier) is associated with loss of control that is subserved by prefrontal control regions including the thalamo-orbitofrontal circuitry and the anterior cingulate gyrus. Finally, drug withdrawal symptoms are thought to be the result of disruptions in frontal cortical circuits that underlie the release of neurotransmitters such as dopamine, serotonin and corticotropin-releasing factors. Whereas PFC activation underlies craving, withdrawal is suggested to be due to deactivation of the PFC. An elaboration of the iRISA model proposed in 2011 (Goldstein & Volkow, 2011) detailed the interactions between the PFC and subcortical regions during behaviors related to addiction. Relative to a healthy, non-drug-abusing state, PFC connectivity creates a conflict during craving and withdrawal states such that drug-related cognitive functions, emotions and behaviors predominate over the non-drug-related Dorsal PFC (“cold” functions) STOP! STOP? GO! Drug-related functions Non-drug-related functions Ventral PFC (“hot” functions) b) Craving and withdrawal a) Healthy state c) Intoxication and bingeing Figure 3.2 The iRISA model depicting the interactions between the PFC and subcortical regions in drug users and non-users. Drug-related neuropsychological functions (e.g. incentive salience, drug wanting, attention bias and drug seeking) that are regulated by these subregions are represented by darker shades and non-drug-related functions (e.g. sustained effort) are represented by lighter shades. Thick arrows depict increases in input and the sizes of circles demonstrate the balance between drug- and non-drug-related functions. (Adapted from Goldstein & Volkow, 2011.) The iRISA Syndrome Model 39

/ functions. These decreased non-drug-related functions (e.g. attention) lead to reduced self-control, anhedonia, stress reactivity and anxiety. During intoxication and bingeing, higher-order non-drug-related cognitive functions are suppressed by increased input from the regions that regulate drug-related, “hot” functions, i.e. there is decreased input from higher-order cognitive control areas, and the “hot” regions come to dominate the higher-order cognitive input. Thus, attention narrows to focus on drug-related cues over all other reinforcers, impulsivity increases and basic emotions – such as fear, anger or love – are unrestrained. The result is that automatic, stimulus-driven behaviors, such as compulsive drug consumption, predominate. The Cue-Elicited Craving Model As characterized by Kalivas and Volkow (2005), craving plays a key role in maintaining addiction. Concretely, this team found that substancerelated cues induce the same neurochemical and behavioral responses as the substance itself. Empirically, neuroimaging studies indicate that craving for these substances occurs within the reward circuitry (Filbey & DeWitt, 2012; Filbey et al., 2009, 2012; Hommer, 1999; Volkowet al., 2002). Specifically, the cue or conditioned stimulus may begin to gain salience within the anterior cingulate (motivation) and the amygdala (emotion). Interoceptive and memory processes may then catalyze activation within the insula and hippocampus, respectively. This subsequently triggers dopamine release from the ventral tegmental area (VTA) to the basal ganglia and cortex, which encodes the learned association between the substance and its salient environmental cues (Filbey & DeWitt, 2012). Finally, the cue-elicited connection is then observed in relevant mesocorticolimbic pathways (e.g. Filbey et al., 2008). The Future of Brain-Behavior Theories of Addiction As with all conceptual models, validation of the theories behind the models is an important step. Consequently, current scientific research is focused largely on these important scientific goals. Challenging the tenet of these models is important in order to continue to make scientific discoveries toward understanding the underpinnings of addiction. As with most disorders that affect behavior, the picture is complex and consists of several factors beyond those that involve the brain. For instance, it is well known that individual differences significantly 40 Brain-Behavior Theories of Addiction

/ influence susceptibility to addiction. This is clearly highlighted by the fact that, although drugs induce changes in the brain, only a small fraction of substance users develop an addiction (~10%). Those who become addicted typically have co-occurring disorders such as mood disorders. A study by Ketcherside and Filbey (2015) addressed this issue by testing the relationship between perceived stress, mood (i.e. depression and anxiety) and problems related to cannabis use. They found that having symptoms of depression and anxiety mediated the relationship between perceived stress and problems with cannabis use. In other words, the mechanism by which the experience of stress then leads to problems with cannabis use is through having symptoms related to depression or anxiety. The implication of this finding is that treatment focused on depression and anxiety symptoms in those with cannabis use problems may prove to be effective, as it is through this pathway that cannabis use problems develop. Beyond biological or psychological factors, it is also important to consider environmental factors. Environmental factors, such as socioeconomic status or peer use, have been shown to influence the development of drug addiction (Figure 3.3). To conclude, taking these non-neurobiological factors into consideration in an evidence-based approach would strengthen current models of addiction that would lead to identifying and, therefore, tackling, these determinants that lead to drug-related problems in the first place. Daily smoking Abstainers Any illicit Cannabis Ecstasy Meth/amphetamines Cocaine 0 5 10 15 Percentage 20 Lowest SES Highest SES 25 30 35 Lifetime risky drinkers Single occasion risk (monthly) Figure 3.3 Daily smoking, risky alcohol consumption and illicit drug use by people with the lowest and highest socioeconomic status (SES), in Australians aged 14 years or older, in 2013. (Adapted from Australian Institute of Health and Welfare, 2014.) The Future of Brain-Behavior Theories of Addiction 41

/ Summary Points • Neurobiological models have evolved to explain the neural adaptations that occur during the progression of addiction from drug intoxication to compulsive drug seeking. • The incentive-sensitization model explains behaviors related to the transition from “liking” to “wanting” a drug. • The allostatic model proposes a framework that takes into account the opponent processes of positive and negative states in addiction. • The iRISA syndrome model integrates higher-order functions in the PFC toward a better understanding of how the complicated behavioral, cognitive and emotional landscape of addiction is modulated by the PFC. • The cue-elicited craving model focuses on the heterogeneity in cognitive processes that underlie continued drug seeking. Review Questions • How do the different models of addiction differ? • What is the difference between “wanting” and “liking” a drug? • What is the primary focus of the allostasis model and what behavioral theory is it based on? • What brain region and associated process does the iRISA model integrate into its framework? • What different cognitive processes does the cue-elicited craving model incorporate? Further Reading Bickel, W. K., Mellis, A. M., Snider, S. E.,et al. (2018). 21st century neurobehavioral theories of decision making in addiction: review and evaluation. Pharmacol Biochem Behav, 164, 4–21. doi:10.1016/j.pbb.2017.09.009 Carey, R. J., Carrera, M. P. & Damianopoulos, E. N. (2014). A new proposal for drug conditioning with implications for drug addiction: the Pavlovian twostep from delay to trace conditioning. Behav Brain Res, 275, 150–156. doi:10.1016/j.bbr.2014.08.053 Dayan, P. (2009). Dopamine, reinforcement learning, and addiction.Pharmacopsychiatry, 42, Suppl. 1, S56–S65. doi:10.1055/s-0028-1124107 42 Brain-Behavior Theories of Addiction

/ DeWitt, S. J., Ketcherside, A., McQueeny, T. M., Dunlop, J. P. & Filbey, F. M. (2015). The hyper-sentient addict: an exteroception model of addiction. Am J Drug Alcohol Abuse, 41(5), 374–381. doi:10.3109/ 00952990.2015.1049701 Di Chiara, G., Bassareo, V., Fenu, S., et al. (2004). Dopamine and drug addiction: the nucleus accumbens shell connection.Neuropharmacology, 47, Suppl. 1, 227–241. doi:10.1016/j.neuropharm.2004.06.032 Garcia Pardo, M. P., Roger Sanchez, C., de la Rubia Orti, J. E. & Aguilar Calpe, M. A. (2017). Animal models of drug addiction. Adicciones, 29(4), 278–292. doi:10.20882/adicciones.862 Lewis, M. D. (2011). Dopamine and the neural“now”: essay and review of addiction: a disorder of choice. Perspect Psychol Sci, 6(2), 150–155. doi:10.1177/1745691611400235 O’Brien, C. P., Childress, A. R., McLellan, A. T. & Ehrman, R. (1992). A learning model of addiction.Res Publ Assoc Res Nerv Ment Dis, 70, 157–177. Robinson, T. E. & Berridge, K. C. (1993). The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Res Brain Res Rev, 18(3), 247–291. doi:10.1016/0165-0173(93)90013-P Weiss, F. (2010). Advances in animal models of relapse for addiction research. In C. M. Kuhn & G. F. Koob, eds.,Advances in the Neuroscience of Addiction, 2nd edn. Boca Raton, FL: CRC Press, pp. 1–26. Spotlight Is addiction a moral failing? An alarming report from the Centers for Disease Control and Prevention (CDC) in 2016 stated that ninety-one Americans die from opioid over dose every day. This figure is higher than deaths from car accidents or gun homicides. In opioid addiction, which has now reached epidemic proportions (i.e. six out of ten drug overdose deaths are due to opioids), 80% developed their addiction after being prescribed opioid medication for pain (Figure S3.1). In other words, in these cases, addiction began with a medical prescription. In turn, the opioid epidemic in the USA has led to muchfinger pointing, with blame put on pharmaceutical companies for creating and aggressively marketing these highly addictive drugs and on physicians who have heavily prescribed the drugs (perhaps not knowing the high risk of addiction). However, the public health response to the opioid epidemic is unlike that of past drug epidemics. Specifically, treatment rather than criminal justice options are provided to those who have opioid addiction. This humane Spotlight 43

/ Figure S3.1 The modern opioid epidemic. (Adapted from NC Department of Health and Human Services, 2016.) 44 Brain-Behavior Theories of Addiction

/ approach to addiction as a public health concern rather than a criminal issue is the approach taken in Poland under their“treat rather than punish” principle. Poland’s National Program for Counteracting Drug Addiction (2011–2016) placed greater emphasis on improving the quality of drug-prevention programs and the quality of life of those undergoing treatment, harm reduction and social reintegration measures. The response to the opioid epidemic in the USA can hopefully lead a change in how addiction is addressed, i.e. by making sure that those with an addiction have access to effective treatment. As important, it is critical that we remove the stigma of addiction and accept that addiction can happen to anyone. References Australian Institute of Health and Welfare. (2014). National Drug Strategy Household Survey detailed report: 2013. Drug statistics series no. 28. Canberra, Australia: Australian Institute of Health and Welfare. Anagnostaras, S. G., Schallert, T. & Robinson, T. E. (2002). Memory processes governing amphetamine-induced psychomotor sensitization. Neuropsychopharmacology, 26(6), 703–715. doi:10.1016/S0893-133X (01)00402-X Boileau, I., Dagher, A., Leyton, M.,et al. (2006). Modeling sensitization to stimulants in humans: an [11C]raclopride/positron emission tomography study in healthy men. Arch Gen Psychiatry, 63(12), 1386–1395. doi:10.1001/archpsyc.63.12.1386 Filbey, F. M., Claus, E., Audette, A. R.,et al. (2008). Exposure to the taste of alcohol elicits activation of the mesocorticolimbic neurocircuitry. Neuropsychopharmacology, 33(6), 1391–1401. doi:10.1038/sj. npp.1301513 Filbey, F. M., Claus, E. D., Morgan, M., Forester, G. R. & Hutchison, K. (2012). Dopaminergic genes modulate response inhibition in alcohol abusing adults. Addict Biol, 17(6), 1046–1056. doi:10.1111/j.1369- 1600.2011.00328.x Filbey, F. M. & DeWitt, S. J. (2012). Cannabis cue-elicited craving and the reward neurocircuitry. Prog Neuropsychopharmacol Biol Psychiatry, 38(1), 30–35. doi:10.1016/j.pnpbp.2011.11.001 Filbey, F. M., Schacht, J. P., Myers, U. S., Chavez, R. S. & Hutchison, K. E. (2009). Marijuana craving in the brain. Proc Natl Acad Sci U S A, 106(31), 13016–13021. doi:10.1073/pnas.0903863106 Goldstein, R. Z. & Volkow, N. D. (2002). Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of References 45

/ the frontal cortex. Am J Psychiatry, 159(10), 1642–1652. doi:10.1176/ appi.ajp.159.10.1642 (2011). Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nat Rev Neurosci, 12(11), 652–669. doi:10.1038/nrn3119 Hommer, D. W. (1999). Functional imaging of craving. Alcohol Res Health, 23(3), 187–196. Kalivas, P. W. & Volkow, N. D. (2005). The neural basis of addiction: a pathology of motivation and choice. Am J Psychiatry, 162(8), 1403–1413. doi:10.1176/appi.ajp.162.8.1403 Ketcherside, A. & Filbey, F. M. (2015). Mediating processes between stress and problematic marijuana use. Addict Behav, 45, 113–118. doi:10.1016/j.addbeh.2015.01.015 Koob, G. F. & Le Moal, M. (1997). Drug abuse: hedonic homeostatic dysregulation. Science, 278(5335), 52–58. (2008). Neurobiological mechanisms for opponent motivational processes in addiction. Philos Trans R Soc Lond B Biol Sci, 363(1507), 3113–3123. doi:10.1098/rstb.2008.0094 Robinson, T. E. & Berridge, K. C. (1993). The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Res Brain Res Rev, 18(3), 247–291. NC Department of Health and Human Services (2016). Jan. 19 task force meeting documents. Available at: www.ncdhhs.gov/document/jan-19- task-force-meeting-documents (accessed August 1, 2017). Solomon, R. L. & Corbit, J. D. (1974). An opponent-process theory of motivation. I. Temporal dynamics of affect.Psychol Rev, 81(2), 119–145. doi:10.1037/h0036128 Volkow, N. D., Fowler, J. S., Wang, G. J. & Goldstein, R. Z. (2002). Role of dopamine, the frontal cortex and memory circuits in drug addiction: insight from imaging studies. Neurobiol Learn Mem, 78(3), 610–624. doi:10.1006/nlme.2002.4099. 46 Brain-Behavior Theories of Addiction

/ CH A PTE R F O U R From the Motivation to Initiate Drug Use to Recreational Drug Use: Reward and Motivational Systems Learning Objectives • Be able to describe the regions within the mesocorticolimbic pathway. • Be able to explain the role of dopamine in reward and motivation. • Be able to identify thefinal common pathway. • Be able to understand the notion of the reward deficiency syndrome. • Be able to discuss the role of memory systems in reward and motivation. Introduction As introduced in Chapter 3, the development of addiction hinges on increased incentive salience (drug “wanting”) placed on the substance of abuse. In other words, compulsive drug-taking behavior occurs at the expense of other activities, whether recreational or occupational. The acquisition of greater incentive salience for drugs (versus other rewarding stimuli) suggests alterations in reward-motivation systems in the brain. In the 1950s, two Canadian physiologists implanted electrodes in specific brain regions of rats (Olds & Milner, 1954). The rats were then given the opportunity to stimulate these brain regions, later termed “reward centers,” by pressing a button. Once they started pressing the stimulation button, they stopped doing anything else, which was the first hint of a strong behavioral reinforcing mechanism (Figure 4.1; see also Figure 2.1). Since then, researchers have shown that this reward center of the brain – the nucleus accumbens – is also involved in drug addiction. Just showing people drug-related pictures led to strong activation in parts of the brain related to craving for the drugs (Filbey et al., 2011). This chapter will describe the first ecological stage of the progression of addiction: initial motivation to use drugs. This chapter will explain the cliché that “drugs hijack the brain” by discussing the various neuroimaging studies that demonstrate this phenomenon.

/ Reward and Motivational Systems Guide the Direction of Behavior The reward and motivational systems contribute toward goal-directed action, allowing organisms to encode the relative values of specific environmental events. These values provide the basis for choice, To shock generator Pellet dispenser (a) (b) Electric grid Lever Speaker Signal lights Dispenser tube Food cup Stimulator Lever press activates stimulator Suspending elastic band Lever Figure 4.1 Lever press (a) and intracranial self-stimulation (ICSS) (b) are two examples of experimental paradigms used to study reward and motivation in animals. (a) Animals learn to press a lever to receive rewards (e.g. food, water, sexual mates, drugs). (b) In ICSS, animals receive electrical stimulation directly into reward areas of the brain, without the influence of specific incentives. These animal paradigms have implicated a role of the mesolimbic dopamine system and its connections with motivational systems. 48 Motivation to Initiate Drug Use to Recreational Drug Use

/ allowing organisms to select actions based on prior knowledge of the consequences of an action, as well as the value of those consequences (see Spotlight for an example of research that examined these systems to identify the risk for addiction). Reward (i.e. feelings of pleasure) and motivation mechanisms that guide directed behavior include anticipation, stimulus evaluation and prediction of reward. Reward and motivation processes occur within a neural circuitry encompassing prefrontal and striatal areas. The key structures within this reward-motivation circuitry are the anterior cingulate cortex, the orbital prefrontal cortex (PFC), the ventral striatum and midbrain dopamine neurons (Figure 4.2). Together, the connections among these areas form a complex neural network that underlies incentive-based or reinforcement learning that leads to goal-directed behaviors and habit formation. The nucleus accumbens encodes the relationship between stimuli and behavioral responses. As such, it is the key region through which salient stimuli exert their reinforcing actions. Evidence for this exists in studies demonstrating increases in dopamine levels in the nucleus accumbens during rewarding behaviors such as eating, drinking and sexual activity. The nucleus accumbens contains two functionally distinct Orbitofrontal cortex Nucleus accumbens Mesolimbic dopamine pathway Mesocortical dopamine pathway Ventral tegmental area (VTA) Figure 4.2 The brain’s reward system lies in the mesocorticolimbic pathway, which is regulated by dopamine. This pathway has dopamine cell bodies in the ventral tegmental area and projects to the nucleus accumbens and areas in the prefrontal cortex, particularly the orbitofrontal cortex. Reward and Motivational Systems 49

/ subregions – the shell and the core. The shell is interconnected with the hypothalamus and ventral tegmental area (VTA), while the core has innervations with the anterior cingulate and orbitofrontal cortex. An interesting finding in animal studies was that different subsets of neurons in the nucleus accumbens respond differentially to encoding “natural” rewards such as water versus cocaine (Carelli et al., 2000). Given the limitations of current techniques for visualization of in vivo responses and the small size of the nucleus accumbens, this finding has not yet been tested in humans. Studies have also demonstrated dendritic changes in the nucleus accumbens following repeated activation that may reflect learning (Figure 4.3) (Robinson & Kolb, 1997). It is therefore likely that these morphological changes in addition to other reported intracellular changes within the nucleus accumbens play a role in the development of addiction. The reciprocal connections between the shell of the nucleus accumbens and the VTA are thought to be important in modulating motivational salience and reinforcement learning. Specifically, when a salient Saline Amphetamine Figure 4.3 Camera lucida drawings of medium spiny neurons in the shell (top) and core (bottom) regions of the nucleus accumbens of saline- and amphetamine-pretreated rats. These cells were selected for illustration because their values were closest to the group average of any cells studied. The drawing to the right of each cell represents a dendritic segment used to calculate spine density. (From Robinson & Kolb, 1997, adapted from Paxinos & Watson, 1997. © 1997 Society for Neuroscience, USA.) 50 Motivation to Initiate Drug Use to Recreational Drug Use

/ event occurs, projections from the VTA release dopamine, which triggers a behavioral response to the motivational event. This process leads to cellular changes that establish learned associations for highly desirable stimuli. Over time, repeated exposure to the same motivational event no longer leads to the same level of dopamine released in response to the event; however, the conditioned stimuli predicting the event continue to trigger the release of dopamine (see Chapter 7 for further details). Unlike the shell of the nucleus accumbens, its core has projections to PFC areas including the anterior cingulate and orbitofrontal cortex. These connections underlie the motivation for rewarding stimuli, thereby contributing to response selection and adaptive learning. Studies have illustrated that the magnitude of change in metabolic activity in both the orbitofrontal and anterior cingulate cortices correlates with the intensity of the self-reported cue-induced craving. Drug specificity of increased prefrontal activity is illustrated by studies demonstrating reduced prefrontal activity during biologically relevant rewards, such as sexually evocative cues and also during decision-making tasks that typically elicit a prefrontal response (Garavanet al., 2000). Thus, dysregulation in the anterior cingulate and orbitofrontal cortex is not only critical for cue-elicited motivation but also in decision making (i.e. cognitive control) over drug seeking (discussed in Chapter 8). Predicting Rewards: Evidence for the Primary Role of Dopamine Based on the circuitry described above, it can be surmised that dopamine plays a key role in reward-motivation processes. Given the brain regions involved during these processes, dopamine can be seen as serving two functions in the circuit: 1) to alert the organism to novel salient stimuli, and thereby promote neuroplasticity (learning); and 2) to alert the organism to an upcoming familiar motivationally relevant event, on the basis of learned associations made with environmental stimuli predicting the event. This is how dopamine has become known as the “pleasure molecule.” Early evidence of dopamine’s role came from cellular recording studies in animals. These studies demonstrated that dopamine neurons fire when an unexpected reward is anticipated but not during the reward itself (Figure 4.4). Dopamine neurons were also inhibited during expected rewards. Based on these studies, it was suggested that dopamine signals aid in learning motivated behavior. In other words, dopamine draws our attention to unexpected positive outcomes for the purpose of promoting rewarded behaviors. Predicting Rewards 51

/ Human research has also provided evidence for the important role of dopamine during reward and motivation. These studies showed that large and fast increases in dopamine levels that are longer in duration and more intense than those induced by dopamine cell firing to other salient events underlie the development of drug addiction. Higher and longer dopamine release potentiates the threshold required for motivational events to activate dopamine neurons, thereby requiring more potent stimuli to reach the prior levels of dopaminergic signaling. Decreases in dopamine release and in dopamine D2 receptors in the striatum also occur following drug use. For example, positron emission tomography (PET) with the D2/3 dopamine receptor ligand antagonist [ 11C]raclopride in combination with methylphenidate (a dopamine reuptake inhibitor, the same as cocaine) showed that methamphetamine abusers had 24% lower levels of dopamine transporters in the striatum compared with people who never used the drug (Volkow et al., 2001). These reductions in striatal extracellular dopamine levels are associated with reduced activity of the orbitofrontal cortex and the cingulate gyrus. Dopamine transporter blocked by cocaine Dopamine Receiving neuron Dopamine receptor Intensity of effect Transmitting neuron Cocaine Figure 4.4 The release of dopamine signals reward. This illustrates mechanisms by which dopamine is released following exposure to cocaine. Cocaine blocks dopamine transporters. Thus, reuptake of dopamine is inhibited, leading to increased levels of dopamine in the synaptic cleft. 52 Motivation to Initiate Drug Use to Recreational Drug Use

/ Interestingly, PET [ 11C]raclopride studies have also shown that, in response to drug-related stimuli (drug cues), these hypoactive prefrontal regions become hyperactive proportionally to the subjective desire for the drug or craving, and may be the mechanism by which“drugs hijack the brain” (discussed further in Chapter 7). Specifically, dopamine release was related to increased motivation, despite the absence of a reward (Volkow et al., 2001). So far, we have discussed how dopamine is critical for acute reward and reinforced learning that leads to addiction. Although, in general, dysfunction of the dopaminergic circuitry may be the neural substrate for the development and maintenance of addiction, an important note is that endstage addiction is primarily due to neural adaptations in glutamatergic projections from the PFC to the nucleus accumbens. Alterations in excitatory input lead to a reduction in the capacity of the PFC to initiate behaviors in response to natural rewards and to provide executive control over drug seeking (lack of control or impulsivity is discussed further in Chapter 8). The hyper-responsivity of the PFC to rewarding stimuli leads to increased glutamatergic input in the nucleus accumbens, where excitatory synapses have a reduced capacity to regulate neurotransmission. Final Common Pathway: All Drugs Lead to One As discussed in the previous section, dopamine is implicated in the initiation and development of drug and alcohol addiction. So how is this possible given the varied neuropharmacological effects of different drugs and alcohol? While cocaine and methamphetamines target dopamine receptors directly, other substances disrupt different parts of the rewardmotivation circuitry. For example, nicotine disrupts the cholingergic system, cannabis disrupts the endocannabinoid system and opiates disrupt the opioid system (see Chapter 5 for a list of specific drug targets). In other words, how do the adaptations in different neural systems disrupt dopamine signaling manifested in addiction? Kalivas and Volkow (2005) proposed a “final common pathway” to answer this question (Figure 4.5). Kalivas and Volkow (2005) proposed that the glutamatergic projection from the PFC to the nucleus accumbens core and ventral pallidum constitute the final common pathway (top path in Figure 4.5) for initiation of drug seeking. This notion was based on experiments showing overlapping yet distinct neurocircuitry underlying cue-, drug- and stressinduced reinstatement of drug-seeking behavior. Reinstatement refers to the resumption of a previously drug-reinforced behavior by exposure to Final Common Pathway: All Drugs Lead to One 53

/ different types of drug cues (cue-induced), drugs (drug-induced) or stressors (stress-induced) after the drug-reinforced behavior has been extinguished. Drug-induced reinstatement involves prefrontal (i.e. dorsomedial) glutamatergic projections to the nucleus accumbens core and dopaminergic projections from the dorsomedial PFC to the nucleus accumbens shell. Cue-induced reinstatement occurs primarily via dopamine and glutamate projections from the VTA, basolateral amygdala, dorsomedial PFC and nucleus accumbens core. Stress-induced reinstatement involves noradrenergic and corticotropin-releasing factor inputs to the central amygdala and bed nucleus of the stria terminalis and nucleus accumbens shell that serially project to the dorsomedial PFC and VTA. In sum, projections from the VTA (all forms of reinstatement), basolateral amygdala (cue reinstatement) and extended amygdala (stress reinstatement) converge on motor pathways involving the dorsomedial PFC and nucleus accumbens core that represents a “final common pathway.” Final common pathway Ventral pallidum Ventral tegmental area Basolateral amygdala Nucleus accumbens core Prefrontal cortex Extended amygdala Central amygdala nucleus, bed nucleus of the stria terminalis, nucleus accumbens shell Cue Stress Figure 4.5 According to Kalivas and Volkow (2005), the projection from the PFC to the nucleus accumbens core to the ventral pallidum is afinal common pathway for drug seeking by increases in dopamine release (via stress, a drug-associated cue or the drug itself) in the PFC. 54 Motivation to Initiate Drug Use to Recreational Drug Use

/ Is Addiction a Reward Deficiency Syndrome? As discussed above, the addiction literature largely supports the notion that dysfunction in the dopaminergic system leads to reduced dopamine levels. This reduction in dopamine levels underlies the compulsion to seek more potent stimuli, such as drugs. The interesting question then becomes, why do only a fraction (i.e. ~10%) of individuals who consume substances become addicted? If highly potent substances, such as drugs and alcohol, lead to the same cascade of events, yet only some individuals develop hypersensitivity to its effects, there are likely risk factors that make some more vulnerable to these effects than others. One of the most-studied risk factors is a potential genetic mechanism, particularly dopaminergic genes. Of the dopaminergic genes, the A1 allele of the dopamine D2 receptor gene (DRD2), which leads to compromised D2 receptors, has been associated with a higher risk for multiple addictive, impulsive and compulsive behavioral propensities, such as severe alcohol, cocaine, heroin, cannabis and nicotine use, glucose bingeing, pathological gambling, sex addiction, attention deficit/hyperactivity disorder (ADHD), Tourette’s syndrome, autism, chronic violence, post-traumatic stress disorder, schizoid/avoidant cluster, conduct disorder and antisocial behavior (Blum et al., 2000). Blum explained the effects of reduced dopamine levels across these various clinical presentations as a reward deficiency syndrome. The reward deficiency syndrome provides a framework by which a breakdown of the reward cascade occurs as a result of both genetic and environmental factors (Blum et al., 2012). The reward deficiency syndrome hypothesis emerged from findings that therapies that increase dopamine levels such as dopamine D2 agonists such as bromocriptine or induction of D 2 -directed mRNA significantly reduce symptoms associated with substance use (e.g. craving, self-administration). Thus, stimulation of D2 receptors resolved the effects of dopamine depletion. Blum and colleagues proposed that D2 receptor stimulation signals a negative feedback mechanism in the mesolimbic system to induce mRNA expression causing proliferation of D2 receptors (Blum et al., 2012). Along with genetic studies demonstrating that dopaminergic polymorphisms of the DRD2 and dopamine transporter (DAT) alleles are associated with behaviors related to dopaminergic depletion (addictive, obsessive, compulsive and impulsive tendencies), the reward deficiency syndrome is proposed as an important phenotype for addiction. Is Addiction a Reward Deficiency Syndrome? 55

/ Corticostriatal Circuitry and Effort–Reward Imbalance While much attention has been placed on the reward-inducing effects of dopamine transmission, there are other aspects of dopaminergic signaling that do not mediate reward processes. For example, studies have also found evidence for the role of dopamine during effort (i.e. lever pressing) but not with the amount of reward. Thus, it is equally important to consider the role of dopamine in behavioral activation and effort. Salamone et al. (2007) postulated that dopamine’s role is to overcome work-related response expenditure. This idea comes from animal research showing how the effects of reduced dopamine in the nucleus accumbens on food-seeking behavior is contingent on how much work is required to accomplish the task. Specifically, in rats, when minimal work was required, lever pressing for food rewards was largely unaffected by dopamine depletions in the nucleus accumbens. In contrast, when the required level of work was high, lever pressing for food rewards was substantially impaired by dopamine depletions in the nucleus accumbens. Interestingly, when dopamine transmission was modulated, rats with reduced dopamine in the nucleus accumbens reallocated their instrumental behavior away from food-reinforced tasks that had high response requirements and instead selected a less effortful type of food-seeking behavior (Figure 4.6). Likewise, dopamine antagonists that block dopamine release, therefore preventing striatal activation, have been found to induce fatigue and reduce motivation. Blocking striatal response leads to a dysregulation of perceived effort vs. perceived gain, referred to as effort–reward calculation (Dobryakova et al., 2013). Role of Memory Systems The research on reward and motivation and how these processes relate to addiction continues to evolve from a model of incentive salience encoding to a functionally more complex model that includes externally and internally driven attention and reward expectancy, as well as prediction errors. This more complicated network suggests an integral role of memory systems, which attempts to resolve the unanswered question of how salient stimuli act on the neural mechanisms of learning and memory underlying reinforcement learning. In other words, how does stored information (i.e. memory) about reinforcing stimuli drive addictive behavior? Animal studies suggest that such 56 Motivation to Initiate Drug Use to Recreational Drug Use

/ information is processed in several independent learning and memory systems. Rewarding stimuli interact with these systems in three ways: 1) they activate neural substrates of observable approach or escape responses; 2) they produce unobservable internal states that can be perceived as rewarding or aversive; and 3) they modulate or enhance the information stored in each of the memory systems (White, 1996). It is suggested that each addictive drug maintains its Control rat (a) (b) (c) (d) Dopamine-depleted rat ?? Lab chow / free access Palatable food /FR 5 Figure 4.6 Experiments on the effects of dopamine depletion on effort. In these studies, animals select between high-effort conditions where highly palatable food reward is accessible through lever pressing (withfixed ratios) or low-effort conditions where less preferred food reward (lab chow) is freely available (a, b). Untreated rats prefer the highly palatable food and lever press, and eat little of the freely available chow (c). This demonstrates preference for high effort/high reward during normal dopamine levels. In contrast, dopamine-depleted rats (through dopamine antagonists) shift their choice from the high-effort condition (lever pressing) to the low-effort condition (freely available chow) (d). This demonstrates the importance of dopamine on effort expenditure. (From Salamoneet al., 2007. © 2007 Springer-Verlag, USA.) Role of Memory Systems 57

/ own self-administration by mimicking some subset of these actions. Evidence demonstrating actions of drugs on multiple neural substrates of reinforcement suggests that no single factor is likely to explain either addictive behavior in general or self-administration in particular. Thus, the basic mechanisms that underlie reward and motivation are similar to those that underlie learning and memory. The dopaminergic and glutamatergic neurotransmitter systems play integrative roles in motivation, learning and memory, thereby modulating adaptive behavior (Kelley, 2004a, 2004b). Summary Points • The mesocorticolimbic pathway underlies reward and motivation processes. • Dopamine is the primary neurotransmitter in the reward-signaling pathway and underlies processes related to the acquisition of positively reinforced behavior. • The final common pathway involves glutamatergic projections from the PFC to striatal regions. • Alterations in the DRD2 gene lead to a reward deficiency syndrome, such as addiction. • Dopamine depletion also leads to changes in perceived effort required for perceived gain. • Reward and motivation systems share mechanisms that underlie learning and memory. Review Questions • What are the brain regions within the mesocorticolimbic pathway and what processes do they underlie? • What has been referred to as the primary reward center of the brain? • What is the evidence suggesting that dopamine is the primary neurotransmitter for reward and motivation? • Explain the final common pathway. • What are the three ways that induce drug reinstatement in animals? • What is the premise behind the theory of reward deficiency syndrome? • What is the role of memory systems in reward and motivation? 58 Motivation to Initiate Drug Use to Recreational Drug Use

/ Further Reading Ekhtiari, H., Nasseri, P., Yavari, F., Mokri, A. & Monterosso, J. (2016). Neuroscience of drug craving for addiction medicine: from circuits to therapies. Prog Brain Res, 223, 115–141. doi:10.1016/bs.pbr.2015.10.002 Filbey, F. M. & DeWitt, S. J. (2012). Cannabis cue-elicited craving and the reward neurocircuitry. Prog Neuropsychopharmacol Biol Psychiatry, 38(1), 30–35. doi:10.1016/j.pnpbp.2011.11.001 Filbey, F. M. & Dunlop, J. (2014). Differential reward network functional connectivity in cannabis dependent and non-dependent users.Drug Alcohol Depend, 140, 101–111. doi:10.1016/j.drugalcdep.2014.04.002 Filbey, F. M., Schacht, J. P., Myers, U. S., Chavez, R. S. & Hutchison, K. E. (2009). Marijuana craving in the brain.Proc Natl Acad Sci U S A, 106(31), 13016–13021. doi:10.1073/pnas.0903863106 Filbey, F. M., Dunlop, J., Ketcherside, A.,et al. (2016). fMRI study of neural sensitization to hedonic stimuli in long-term, daily cannabis users.Hum Brain Mapp, 37(10), 3431–3443. doi:10.1002/hbm.23250 Franken, I. H. (2003). Drug craving and addiction: integrating psychological and neuropsychopharmacological approaches.Prog Neuropsychopharmacol Biol Psychiatry, 27(4), 563–579. doi:10.1016/S0278-5846(03) 00081-2 Gu, X. & Filbey, F. (2017). A Bayesian observer model of drug craving.JAMA Psychiatry, 74(4), 419–420. doi:10.1001/jamapsychiatry.2016.3823 Heinz, A., Beck, A., Mir, J., et al. (2010). Alcohol craving and relapse prediction: imaging studies. In C. M. Kuhn & G. F. Koob, eds.,Advances in the Neuroscience of Addiction, 2nd edn. Boca Raton, FL: CRC Press, pp. 137–162. Robinson, T. E. & Berridge, K. C. (1993). The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Res Brain Res Rev, 18(3), 247–291. doi:10.1016/0165-0173(93)90013-P Sinha, R. (2009). Modeling stress and drug craving in the laboratory: implications for addiction treatment development. Addict Biol, 14(1), 84–98. doi:10.1111/j.1369-1600.2008.00134.x Wise, R. A. (1988). The neurobiology of craving: implications for the understanding and treatment of addiction. J Abnorm Psychol, 97(2), 118–132. doi:10.1037/0021-843X.97.2.118 Further Reading 59

/ Spotlight Motivated to predict future drug abuse Early intervention for substance use disorder is key to treatment success and is the reason why much research is dedicated toward identifying ways to predict risk for developing addiction. If a person’ssusceptibility to addiction was known, effective preventative strategies could be applied. Knowledge of the risk for addiction can inform targeted treatment. Forexample, knowing the mechanisms that led to the disorder can lead to timely and effective interventions. A group of scientists from Stanford aimed to determine whether risk for drug addiction could be identified using brain response patterns in 14-yearolds with high novelty seeking. Novelty seeking is an attribute that promotes independence and is therefore beneficial during adolescence. This is why although novelty seeking has also been associated with later development of drug addiction, not everyone who is novelty seeking becomes addicted to drugs. The question then becomes, what makes novelty seeking in adolescence a risk for drug addiction? To answer this question, Büchelet al. (2017) used functional magnetic resonance imaging (fMRI; see Chapter 2) to test whether brain responses in the brain’s motivational areas in 144 14-year-olds predicted drug abuse at age 16. Using the monetary incentive delay task (Figure S4.1), which measures the response to monetary gains, the researchers found that the 14-year-old adolescents who showed reduced motivational activity during monetary gain were more likely to abuse drugs by the time they were 16 years old. In other words, insufficient activation in motivation areas in novelty-seeking adolescents may be a predictor of later drug abuse. (a) 60 Motivation to Initiate Drug Use to Recreational Drug Use

/ Magnitude cue shapes: $0 250 ms 160–260 ms 1.75–14 s ~2–14 s Win 1 s $1 $10 2.12 s 1 s 0–12 s ITI Feedback Hit/win cue Target Magnitude cue ITI 250 ms 160–260 ms 1.75–14 s ~2–14 s 1 s 2.12 s 1 s 0–12 s ITI Feedback Hit/win cue Hit? Win? (+$1.00) $11.00 (+$0.00) $10.00 $ $ + + Target Magnitude cue + + + + + + + + (b) Figure S4.1 (a) Sensation and novelty seeking are characteristic of adolescence. (b) Schematic of the monetary incentive delay task. This is a widely utilized task to measure brain responses during motivated behavior. In this task, participants win or avoid losing money if they are able to press a button while the target (the white square in this illustration) is present. The task not only provides researchers with the ability to measure responses during monetary wins and losses but is also able to determine if the magnitude of the reward (i.e. different amounts of money: $0, $1 or $10 in this illustration) influences response. ITI, intertrial interval. References Blum, K., Braverman, E. R., Holder, J. M.,et al. (2000). Reward deficiency syndrome: a biogenetic model for the diagnosis and treatment of impulsive, addictive, and compulsive behaviors. J Psychoactive Drugs, 32, Suppl. 1, p. i-iv, 1–112112. Blum, K., et al.Gardner, E., Oscar-Berman, M. & Gold, M. (2012).“Liking” and “wanting” linked to Reward Deficiency Syndrome (RDS): hypothesizing differential responsivity in brain reward circuitry.Curr Pharm Des, 18(1), 113–118. doi:10.2174/138161212798919110 References 61

/ Büchel, C., Peters, J., Banaschewski, T., et al. (2017). Blunted ventral striatal responses to anticipated rewards foreshadow problematic drug use in novelty-seeking adolescents. Nat Commun, 8, 14140. doi:10.1038/ ncomms14140 Carelli, R. M., Ijames, S. G. & Crumling, A. J. (2000). Evidence that separate neural circuits in the nucleus accumbens encode cocaine versus “natural” (water and food) reward. J Neurosci, 20(11), 4255–4266. doi:10.1523/JNEUROSCI.20-11-04255.2000 Dobryakova, E., DeLuca, J., Genova, H. M. & Wylie, G. R. (2013). Neural correlates of cognitive fatigue: cortico-striatal circuitry and effortreward imbalance. J Int Neuropsychol Soc, 19(8), p. 849–853. doi:10.1017/S1355617713000684 Filbey, F. M., Claus, E. D. & Hutchison, K. E. (2011). A neuroimaging approach to the study of craving. In: Adinoff, A. & Stein, E., eds. Neuroimaging in Addiction. London: Wiley-Blackwell, pp. 133–156. Garavan, H., Pankiewicz, J., Bloom, A., et al. (2000). Cue-induced cocaine craving: neuroanatomical specificity for drug users and drug stimuli. Am J Psychiatry, 157(11), 1789–1798. doi:10.1176/appi. ajp.157.11.1789 Kalivas, P. W. & Volkow, N. D. (2005). The neural basis of addiction: a pathology of motivation and choice. Am J Psychiatry, 162(8), 1403–1413. doi:10.1176/appi.ajp.162.8.1403 Kelley, A. E. (2004a). Memory and addiction: shared neural circuitry and molecular mechanisms. Neuron, 44(1), 161–179. doi:10.1016/j. neuron.2004.09.016 (2004b). Ventral striatal control of appetitive motivation: role in ingestive behavior and reward-related learning. Neurosci Biobehav Rev, 27(8), 765–776. doi:10.1016/j.neubiorev.2003.11.015 Olds, J. & Milner, P. (1954). Positive reinforcement produced by electrical stimulation of septal area and other regions of rat brain.J Comp Physiol Psychol, 47(6), 419–427. doi:10.1037/h0058775 Paxinos, G. & Watson, C. (1997). The Rat Brain in Stereotaxic Coordinates, 3rd edn. New York, NY: Academic Press. Robinson, T. E. & Kolb, B. (1997). Persistent structural modifications in nucleus accumbens and prefrontal cortex neurons produced by previous experience with amphetamine. J Neurosci, 17(21), 8491–8497. doi:10.1523/JNEUROSCI.17-21-08491.1997 Salamone, J. D., Correa, M., Farrar, A. & Mingote, S. M. (2007). Effortrelated functions of nucleus accumbens dopamine and associated forebrain circuits. Psychopharmacology (Berl), 191(3), 461–482. doi:10.1007/s00213-006-0668-9 62 Motivation to Initiate Drug Use to Recreational Drug Use

/ Volkow, N. D., Chang, L., Wang, G. J. et al. (2001). Loss of dopamine transporters in methamphetamine abusers recovers with protracted abstinence. J Neurosci, 21(23), 9414–9418. doi:10.1523/ JNEUROSCI.21-23-09414.2001 White, N. M. (1996). Addictive drugs as reinforcers: multiple partial actions on memory systems. Addiction, 91(7), 921–949; discussion 951–65. doi:10.1046/j.1360-0443.1996.9179212.x References 63

/ CH A PTER F IVE Intoxication Learning Objectives • Be able to explain the concept of intoxication. • Be able to understand the principles of pharmacodynamics. • Be able to discuss the actions of each drug class. • Be able to summarize the effects of intoxication on glucose metabolism, cerebral blood flow, brain function and electrophysiology. • Be able to describe the modulators of intoxication effects. Introduction Drug intoxication refers to the immediate effects of the drug and occurs during consumption of a drug in a large enough dose to produce signi ficant behavioral, physiological or cognitive impairments. It is these intoxicating effects that drive initial drug use. When drugs and alcohol are consumed, a cascade of short- and long-term effects follows. Although some of the effects of intoxication are pleasant and desired, other effects can be aversive (Figure 5.1). For example, alcohol intoxication or the state of being “drunk” manifests as facialflushing, slurred speech, unsteady gait, euphoria, increased activity, volubility, disorderly conduct, slowed reactions, impaired judgement and motor incoordination, insensibility and stupefaction. Understanding the effects of intoxication on the brain can inform how this process contributes to drug addiction. This chapter will discuss the mechanisms that underlie these intense feelings of pleasure that occur while taking some of the most common substances of abuse including alcohol, nicotine, cannabis and cocaine. According to the ICD-10, “intoxication is a condition that follows the administration of a psychoactive substance and results in disturbances in the level of consciousness, cognition, perception, judgement, affect, or behavior, or other psychophysiological functions and responses ” (World

/ Health Organization, 2004). Disturbances result from the direct pharmacological effects of the drug, as well as through learned experiences. Acute intoxication is transient and is positively correlated with dose levels. The intensity of intoxication lessens with time, and the effects eventually disappear in the absence of further use of the substance. Symptoms of intoxication are not always reflective of the primary actions of the substance. For instance, depressant drugs may lead to symptoms of agitation or hyperactivity, and stimulant drugs may lead to socially withdrawn and introverted behavior. Some drugs, such as cannabis and hallucinogens, may lead to unpredictable effects, while many psychoactive substances can produce different types of effects at different levels of intoxication. A clear example of this latter effect is during alcohol intoxication, which is associated with energetic effects at low dose levels that could lead to agitation at medium dose levels and at sedation at higher levels. Figure 5.1 Alcohol intoxication may impact sensorimotor skills. Introduction 65

/ Drug Pharmacodynamics To begin to understand the specific effects of addictive drugs on the brain and behavior, it is first important to understand the principles of pharmacodynamics. Pharmacodynamics refers to the mechanisms of drugs at both organ and cellular levels. It also refers to dose–effect relationships, as well as interactive effects of drugs. The majority of drugs interact with target biomolecules, such as enzymes, ion channels and transporters through receptor binding. Receptors are macromolecules located on the cell surface whose function is to recognize drug signals and initiate a response (i.e. transduction). Drugs can be classified based on the receptor’s response to them (see Figure 5.2): agonists activate receptors; antagonists block the action of an agonist on the receptor; inverse agonists activate receptors to produce an effect in the opposite direction of an agonist; partial agonists activate a receptor but only at a submaximum level while also blocking the action of a full agonist; and ligands have selective binding to specific receptors or sites. There are four classes of receptor that can transduce a signal to a response: G protein-coupled receptors, ion-channel receptors, enzymelinked receptors and receptors of gene expression. Actions of Addictive Drugs Although the feeling of a “high” or “rush” immediately following drug consumption is associated with increases in extracellular dopamine in the striatum, particularly the nucleus accumbens (see Chapter 4), different substances have discrete mechanisms of action. Stimulants target different molecules. For example, amphetamines, cocaine, lysergic acid diethylamide (LSD) and 3,4-methylenedioxymethamphetamine (MDMA) can increase dopamine by triggering dopamine release or blocking dopamine transporters. Dopamine transporters are the main mechanism for recycling dopamine back into the nerve terminals. Elevated levels of dopamine lead to feelings of alertness and happiness, and reduce feelings of hunger (see Chapter 4 on cocaine’s action on dopamine transporters). Amphetamines, cocaine and LSD also increase serotonin levels. Increased levels of serotonin result in feelings of happiness and fullness. Increased serotonin also provides pain relief. Finally, amphetamines and cocaine also act as norepinephrine receptor agonists, triggering increased heart rate, alertness and happiness, and decreasing blood circulation and pain. Nicotine is also a stimulant that acts as a receptor agonist at nicotinic acetylcholine receptors (nAChRs), 66 Intoxication

Antagonistic drug effects Drug increases the synthesis of neurotransmitter molecules Agonistic drug effects Drug increases the number of neurotransmitter molecules by destroying degrading enzymes Drug binds to autoreceptors dblkthiihibitfftDrug increases release of neurotransmitters from terminal buttons Drug blocks the synthesis of neurotransmitters Drug causes neurotransmitter to leak from the vesicles and be destroyed by degrading enzymes Drug activates autoreceptors andinhibitsneurotransmitterDrug blocks the release of the neurotransmitter from terminal buttons

/ and blocks their inhibitory effect on neurotransmitter release Drug binds to postsynaptic receptors and either activates them or increases the effect on them of neurotransmitters Drug blocks the deactivation of neurotransmitters by blocking degradation or reuptake and inhibits neurotransmitter release Drug is a receptor blocker; it binds to the postsynaptic receptors and blocks the effect of the neurotransmitter Figure 5.2 Mechanisms of drug action.

/ particularly α4β2 but not α4β9 and α4β10 receptors, where it acts as a receptor antagonist. α4β2 receptors are present on dopamine neurons, and may be the mechanism through which nicotine exerts its reinforcing effects. Activation of nAChRs leads to increased acetylcholine, which modulates other neurotransmitter functions and is associated with increased memory, muscle contractions, sweat and saliva secretions, and decrease heart rate. Sedatives or depressants, such as alcohol, barbiturates and benzodiazepines, increase dopamine indirectly through their effects on γ-aminobutyric acid (GABA) receptors, which decrease the excitability of neurons. This action promotes decreased brain function, inducing sleepiness and reducing anxiety, alertness, memory and muscle tension. Sedative-anesthetic drugs such as phencyclidine (PCP) and ketamine are N-methyl-d-aspartate (NMDA) receptor (a type of glutamatergic receptor) antagonists. The primary effect is increased excitatory transmission, which leads to visual and auditory distortions (hallucinations), as well as perceptual changes at higher doses (dissociations or feelings of detachment). Opiates such as morphine, heroin and hydrocodone bind to μ-opioid receptors present on dopamine and GABA neurons, thus regulating dopamine function. μ-Opioid receptor binding leads to sedation, increasing sleepiness and reducing anxiety and pain. Tetrahydrocannabinol (THC) in cannabis is a partial agonist at cannabinoid 1 (CB1) receptors that modulate dopamine cells and postsynaptic dopamine signaling. The effects of THC on CB1 receptors include increased hunger, happiness and calmness, but it can also lead to unusual thoughts and feelings. Moreover, the modulatory role of CB1 receptors on dopamine functioning provides a possible mechanism through which THC may increase the reinforcing effects of other drugs of abuse, such as alcohol, nicotine, cocaine and opioids. Brain Mechanisms of Intoxication: Evidence From Neuroimaging Pharmacological Studies Neuroimaging approaches (described in Chapter 2) have advanced our understanding of the brain mechanisms that underlie the intoxicating effects of addictive drugs in humans. These paradigms typically involve a single-dose administration and combine functional neuroimaging approaches with self-reports (questionnaires or clinical interviews) to track brain function with subjective experience related to acute intoxication. Thus, although animal studies have been able to provide extensive evidence that drug intoxication is related to disruption of dopamine levels, only human neuroimaging studies can integrate these findings 68 Intoxication

/ with the behavioral manifestations of drug intoxication (e.g. highs and craving). The biggest challenge for human neuroimaging research involves the temporal issues surrounding acute pharmacological effects. This is one of the reasons why substances such as nicotine and alcohol that pervade the brain quickly and have a short duration of effect relative to other substances have been widely studied. Some of the first in vivo studies illustrating the acute effects of drugs in the human brain utilized electroencephalography (EEG) techniques. These studies provided evidence for the diverse mechanisms by which substances of abuse target the brain. Alterations in different eventrelated potential (ERP) components have been observed following acute administration of cannabis, alcohol and cocaine (Porjesz & Begleiter, 1981; Roth et al., 1977). EEG recordings during nicotine administration have indicated shifts from low to high frequencies. Specifically, Domino (2003) administered an average nicotine yield cigarette to overnightabstinent smokers and found decreased EEG α1 , δ and θ frequencies but increased α2 and β frequency amplitudes, indicating increased arousal and alertness after nicotine exposure. EEG studies on alcohol, however, found opposite effects, with alterations primarily in lowerfrequency bands. For example, low doses of ethanol (0.75 mg/kg) at 90 min post-consumption in young adult males increased power in the θ (4–7 Hz) and α (7.5–9 Hz) frequency bands (Ehlers et al., 1989). Interestingly, those with high amounts of fast α activity prior to ethanol administration reported having fewer feelings of intoxication after ethanol than those with lower amounts of pre-drug fast α waves (9–12 Hz). In sum, it appears that increases inα frequency may underlie the feelings of euphoria during acute intoxication (Lukas et al., 1995). In addition to EEG, positron emission tomography (PET) and singlephoton emission computed tomography (SPECT) techniques have allowed the visualization of acute drug effects at the neuronal receptor level. These studies have provided information on displacement of labeled, receptor-specific ligands, allowing visualization of receptor regulation in affected circuits. Several studies have shown the acute effects of alcohol on dopamine levels. In smokers, PET studies have demonstrated a dose-dependent effect on nAChR binding. For example, Brody et al. (2006) used 2-[18F]fluoro-3-(2(S)-azetidinylmethoxy) pyridine as a ligand for nAChRs during PET to determine β2 * nAChRs (nAChRs containing the β2 * subunit, where * represents other subunits that may also be part of the receptor) occupancy following varying amounts of nicotine (none, one puff, three puffs, one full cigarette, or to satiety [two and a half to three cigarettes]). They found a linear relationship between the amount Brain Mechanisms of Intoxication 69

/ of cigarette smoke exposure and β2* nAChR occupancy (Figure 5.3). They further noted that β2* nAChR binding lasted for up to 3.1 h after exposure, suggesting long-lasting saturation of β2 * nAChRs. Similar prolonged effects on β2 * nAChR occupancy has been reported using the chemical 5-[123I]iodo-85380 to quantify nAChRs during SPECT (Esterlis et al., 2010). They found 67Æ9% (range 55–80%) receptor occupancy after subjects had smoked to satiety (~2.4 cigarettes). Of note, these studies were conducted in experienced smokers, and thus findings may be different in naïve users. However, studies of second-hand smoke have reported similar nAChR occupancy in both smokers and nonsmokers (Figure 5.3c). PET can also inform on how substances affect the brain’s energy utilization or glucose metabolism (the brain’s primary energy source). (b) (c) (a) 0.0 Cigarette 0.1 Cigarette 0.3 Cigarette 1.0 Cigarette 3.0 Cigarette 0 10 0 Q-1 (2.6 ng/ml) Q-3 (0.4 ng/ml) No smoking (0.0 ng/ml) MRI T1-weighted MRI Control Second-hand smoke 0 10 Vs /fp kBq 9 kBq/mL MRI 2-FA PET imaging of nAChR occupancy from cigarette smoke exposure Figure 5.3 PET studies to determine the effects of nicotine administration. (a) Nicotine intake leads to dose-dependent occupancy ofα4β2* nAChRs (noted by progressively decreasing nAChR binding in blue with increased dose). (b) Low-nicotine cigarettes result in 26% and 79% α4β2* nAChR occupancies. (c) Moderate second-hand smoke exposure results in 19% occupancy ofα4β2* nAChRs in smokers (shown) and non-smokers (not shown). 2-FA, 2-[18F]fluoro-3-(2(S)-azetidinylmethoxy) pyridine; MRI, magnetic resonance imaging. (From Jasinska et al., 2014.) (A black and white version of thisfigure will appear in some formats. For the color version, please refer to the plate section.) 70 Intoxication

/ In cocaine abusers, acute cocaine administration, and in heavy drinkers (and controls) acute alcohol administration decreases brain glucose metabolism (Volkow et al., 1990). Many studies have shown that low to moderate doses of alcohol (0.25–0.75 g/kg) significantly reduce glucose metabolism in the brain, from 10% to 30%, especially in the occipital cortex (for visual processing) and cerebellum (for movement and balance) (Volkow et al., 2006; Wang et al., 2000). Interestingly, this change in glucose metabolism is network specific, such that moderate doses of alcohol (0.75 g/kg) decreased absolute whole-brain metabolism but increased metabolism in reward-motivation regions such as the striatum (including the nucleus accumbens) and the amygdala. Given this decrease in glucose metabolism following acute alcohol intake (hypoglycemia), what does the brain use for energy? Research has suggested that acetate may be an alternative brain energy source to glucose during acute alcohol intoxication (Volkow et al., 2013). This was discovered during an alcohol challenge study, where the brain areas showing the largest decreases in [18F]fluorodeoxyglucose had the largest increases in [1– 11C]acetate brain uptake. In addition to changes in glucose metabolism, PET has also provided information on the effects of addictive drugs on brain blood flow. PET studies have shown that these effects do not involve the entire brain but are regionally specific. Studies in alcohol have shown increases in cerebral blood flow after varying doses of alcohol in prefrontal and temporal regions (Sano et al., 1993; Tolentino et al., 2011). In contrast, cerebral blood flow appears to decrease in the cerebellum (Ingvar et al., 1998). Another way to measure brain activity besides cerebral blood flow changes is through fluctuations in functional connectivity via functional magnetic resonance imaging (fMRI). More specifically, resting-state functional connectivity (rsFC) during fMRI is a technique whereby functional connectivity during the resting state (rather than during performance of a task), also referred to as intrinsic connectivity, is inferred as the temporal correlation between activated regions in the brain. rsFC studies following acute intravenous alcohol infusion have shown increased intrinsic connectivity in an auditory network (temporal lobe and anterior cingulate cortex), as well as in the visual cortex network (Esposito et al., 2010). These studies took into consideration the vascular effects of the drugs, which can confound cerebral blood flow. For example, the vasoconstricting properties of cocaine could decrease cerebral blood flow. fMRI studies can also evaluate how acute intoxication can affect brain function during tasks as opposed to during the resting state, as discussed Brain Mechanisms of Intoxication 71

/ above. Some of the earliest studies examined the brain’s response to simple visual and auditory stimulation following alcohol administration (Levin et al., 1998; Seifritz et al., 2000) and reported brain activation reductions (via the BOLD response; see Chapter 2) in respective visual and auditory cortices following alcohol administration. Later studies have also reported similar decreases in neural response effects during cognitive or emotional tasks after alcohol consumption. For example, alcohol intake increased the time it took to respond to an attention task and increased commission and omission errors (Anderson et al., 2011). Dose-dependent reductions in brain response were also noted across several brain regions including the insula, lateral prefrontal cortex and parietal lobe. Similar dose-related decreases in neural activation in driving-associated brain regions that correlated with driving performance have also been reported (Meda et al., 2009). An example of a virtual reality driving simulator device is shown in Figure 5.4. Meda et al. (2009) tested driving performance using such a device during fMRI at different blood alcohol concentrations. The findings revealed dosedependent disruptions in the spatiotemporal (superior, middle and orbitofrontal gyri, anterior cingulate, primary/supplementary motor areas, basal ganglia and cerebellum) neural response during driving, especially at high doses (0.10% blood alcohol concentration). In terms of driving performance, white line crossings and mean speed also demonstrated significant dose-dependent changes. Altogether, these task-activation fMRI studies suggest that alcohol reduces brain activity through significant functional alterations in brain regions involved in attention, perception, and motor planning and control. Figure 5.4 Example of a virtual reality driving simulator device. (From Fan et al., 2018.) 72 Intoxication

/ In terms of emotional processing during intoxicated states, alcohol fMRI studies indicate that alcohol blunts the brain’s response to emotional stimuli. For example, Gilman et al. (2008) reported that a blood alcohol content of 0.08% (following ethanol infusion) led to an undifferentiated response during viewing of fearful or neutral faces in regions important for emotional processing (amygdala, insula and parahippocampal gyrus) (Figure 5.5). There has also been evidence for lack of amygdala response – a critical area for emotion recognition – while viewing threatening faces (e.g. angry, fearful) (Sripadaet al., 2011). Modulators of Intoxication: Challenges in Human Research It is important to note that there is wide individual variability in the presentation of intoxicating effects of drugs and alcohol. This is due to Corpus callosum Lateral ventricle Third ventricle Third ventricle Optic tract Medial medullary lamina (a) Intermediate mass Choroid plexus Fornix Amygdaloid nucleus Corpora mamillaria Thalamus Globus pallidus Caudate nucleus Putamen Claustrum Insula Internal capsule Figure 5.5 (a) Position of the amygdala (arrow). (b). Response in brain regions to emotional faces during alcohol intoxication. Asterisks indicate statistically significant differences in the level of activation. (Part (b) from Gilmanet al., 2008. © 2008 Society for Neuroscience, USA.) Modulators of Intoxication: Challenges in Human Research 73

/ several factors that interact with the mechanisms that underlie intoxication. These factors could be: 1) context dependent, e.g. rate of consumption, concentration or potency of the drug; 2) individual characteristics, e.g. sex, age or genetics; or 3) state dependent, e.g. expectancies, or adaptations to substance use (e.g. tolerance) (see Spotlight on how these factors pose challenges for drug policies). The speed with which a drug acts depends on the dose taken, the mode of administration, and the rate of clearance to and from the brain. Intravenous Striatal areas of interest Visual–emotional areas of interest Alcohol fearful Alcohol neutral Placebo fearful Placebo neutral 0.12 * * ** ** ** * * *** * *** * Percentage signal change relative to baseline Percentage signal change relative to baseline 0.1 0.08 0.06 0.04 0.2 0 –0.02 –0.04 –0.06 –0.08 –0.1 0.35 0.3 0.25 0.02 0.15 0.1 0.05 0 –0.05 –0.1 Amygdala Nucleus accumbens Putamen Caudate Lingual gyrus (left) (right) Fusiform gyrus (left) (right) (left) (right) (left) (right) (left) (right) (left) (right) Alcohol fearful Alcohol neutral Placebo fearful Placebo neutral (b) Figure 5.5 (cont.) 74 Intoxication

/ delivery leads to the fastest drug effects because the drug reaches the brain more quickly. The response to drugs is also related to previous drug experiences. For example, the magnitude of intoxication (i.e. the increase in dopamine) attenuates with greater severity of substance use. Acute administration of methylphenidate, for example, increased levels of glucose metabolism in prefrontal-striatal areas in active cocaine abusers with low D2 receptor availability (Volkow et al., 1999) but decreased it in non-addicted individuals (Volkow et al., 2005). Individual differences in personality traits as well as drug expectancies – the expected effect of a drug – can also influence intoxicated behavior and may interfere with the pharmacodynamic properties of drugs. Females are also typically more sensitive to intoxicating effects of drugs, perhaps due to general differences in body weight, percentage body fat or rate of renal clearance of unchanged drug (which is decreased in females due to a lower glomerular filtration rate or flow rate of fluid through the kidney). Similar age effects may be due to a reduction in renal and hepatic clearance with increasing age. Last, dopamine sensitivity based on underlying genetic factors can also influence the response to the intoxicating effects of drugs. This notion suggests that genetic variations in the dopamine D2 receptor gene (DRD2) allele may lead to hypersensitivity of dopamine release, leading to increased likelihood of relapse (Blum et al., 2009). In other words, dopaminergic agonists may result in stronger activation of brain reward circuitry in those who carry the DRD2 A1 allele compared with the DRD2 A2 allele because those with the A1 allele have significantly lower D2 receptor density (see reward deficiency syndrome in Chapter 4). Summary Points • The specificity of drug targets lead to the varied intoxication effects. • Brain bloodflow during intoxication is region specific. • There is a reduction in glucose metabolism during intoxication that is correlated with increases in acetate in the same regions. • Levels of intoxication are due to many factors that are: 1) context dependent; 2) individual dependent; or 3) state dependent. • Intoxicated driving is due to dose-related decreases in neural activation that are correlated with driving performance, especially at high doses. Summary Points 75

/ Review Questions • Describe the specific mechanisms leading to various intoxicating effects of each drug class type. • What can factors that influence differences in intoxication effects be categorized into? • In general, what do EEG studies show in terms of changes in brain electrophysiology during intoxication? • How is cerebral blood flow impacted during intoxication? • What happens to glucose and acetate during intoxicated states? • Describe the neural underpinnings of intoxicated driving. • What mechanisms underlie the emotional symptoms during intoxication? Further Reading Calhoun, V. D., Pekar, J. J. & Pearlson, G. D. (2004). Alcohol intoxication effects on simulated driving: exploring alcohol-dose effects on brain activation using functional MRI. Neuropsychopharmacology, 29(11), 2097–2017. doi:10.1038/sj.npp.1300543 Hsieh, Y. J., Wu, L. C., Ke, C. C.,et al. (2018). Effects of the acute and chronic ethanol intoxication on acetate metabolism and kinetics in the rat brain. Alcohol Clin Exp Res, 42(2), 329–337. doi:10.1111/acer.13573 Mathew, R. J., Wilson, W. H., Coleman, R. E., Turkington, T. G. & DeGrado, T. R. (1997). Marijuana intoxication and brain activation in marijuana smokers. Life Sci, 60(23), 2075–2089. doi:10.1016/S0024-3205(97)00195-1 Volkow, N. D., Kim, S. W., Wang, G. J.,et al. (2013). Acute alcohol intoxication decreases glucose metabolism but increases acetate uptake in the human brain. Neuroimage, 64, 277–283. doi:10.1016/j.neuroimage.2012.08.057 Volkow, N. D., Wang, G. J., Fowler, J. S.,et al. (2000). Cocaine abusers show a blunted response to alcohol intoxication in limbic brain regions.Life Sci, 66(12), PL161–167. doi:10.1016/S0024-3205(00)00421-5 Spotlight Buzz Kill The legalization of cannabis for recreational use in California made the state the world’s largest cannabis market. One of the challenges this brings is to 76 Intoxication

/ law enforcement, which has the responsibility of ensuring the safety of Californian roads from intoxicated drivers (Figure S5.1). Californian police are now trained on how to identify cannabis-impaired drivers without the help of objective measures, because, unlike a quantifiable marker of legal limits such as blood alcohol level (0.08% in California), there is no presumed level of intoxication in California, and intoxication and cognitive and motor impairment are highly variable among individuals. Although some Californian police departments are using saliva tests, a blood sample is the only method that provides quantification of THC in the system. Blood testing is currently a voluntary test in California that drivers can refuse. All of these efforts may be in vain, given the number of factors that contribute toward measurable levels, which consequently diminish the meaningfulness of these tests. These factors include how the cannabis was consumed and metabolized. In the end, the best current method is to train law enforcement officers to spot signs of impairment. Drugged driving screening looks for cognitive changes among twelve different steps. For instance, suspects are told to tip back their heads and estimate when 30 s have passed; some drugs make time seem to slow down, while other drugs produce the sensation that time has accelerated, affecting the user’s perception. The California Highway Patrol and other agencies also are cooperating with the Center for Medicinal Cannabis Figure S5.1 Law enforcement challenges during changes in cannabis legislation. (From https://www.pexels.com/photo/auto-automobile-blur-buildings-532001/.) Spotlight 77

/ Research at the University of California, San Diego. The center is analyzing and trying to improve both the human drug-recognition experts and the saliva testing as part of a 2-year, $1.8 million study. Researchers are giving 180 volunteers cannabis with varying levels of potency, and then measuring both their performance in a driving simulator and ways of spotting any impairment. They also are trying to learn whether there is a particular presumptive level of cannabis intoxication that impairs driving. References Anderson, B. M., Stevens, M. C., Meda, S. A.,et al. (2011). Functional imaging of cognitive control during acute alcohol intoxication. Alcohol Clin Exp Res, 35(1), 156–165. doi:10.1111/j.1530-0277.2010.01332.x Blum, K., Chen, T. J., Downs, B. W., et al. (2009). Neurogenetics of dopaminergic receptor supersensitivity in activation of brain reward circuitry and relapse: proposing “deprivation-amplification relapse therapy” (DART). Postgrad Med, 121(6), 176–196. doi:10.3810/ pgm.2009.11.2087 Brody, A. L., Mandelkern, M. A., London, E. D.,et al. (2006). Cigarette smoking saturates brain α4β2 nicotinic acetylcholine receptors. Arch Gen Psychiatry, 63(8), 907–915. doi:10.1001/archpsyc.63.8.907 Domino, E. F. (2003). Effects of tobacco smoking on electroencephalographic, auditory evoked and event related potentials. Brain Cogn, 53(1), 66–74. doi:10.1016/S0278-2626(03) 00204-5 Ehlers, C. L., Wall, T. L. & Schuckit, M. A. (1989). EEG spectral characteristics following ethanol administration in young men. Electroencephalogr Clin Neurophysiol, 73(3), 179–187. doi:10.1016/ 0013-4694(89)90118-1 Esposito, F., Pignataro, G., Di Renzo, G.,et al. (2010). Alcohol increases spontaneous BOLD signal fluctuations in the visual network. Neuroimage, 53(2), 534–543. doi:10.1016/j.neuroimage.2010.06.061 Esterlis, I., Cosgrove, K. P., Batis, J. C.,et al. (2010). Quantification of smoking-induced occupancy of β2-nicotinic acetylcholine receptors: estimation of nondisplaceable binding. J Nucl Med, 51(8), 1226–1233. doi:10.2967/jnumed.109.072447 Fan, J., Chen, S., Liang, M. & Wang, F. (2018). Research on visual physiological characteristics via virtual driving platform.Adv Mech Eng, 10(1), 1687814017717664. doi:10.1177/1687814017717664 Gilman, J. M., Ramchandani, V. A., Davis, M. B., Bjork, J. M. & Hommer, D. W. (2008). Why we like to drink: a functional magnetic resonance 78 Intoxication