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

/ seeking personality traits. Biol Psychiatry, 68(8), 770–773. doi:10.1016/ j.biopsych.2010.06.015 Gerbing, D. W., Ahadi, S. A. & Patton, J. H. (1987). Toward a conceptualization of impulsivity: components across the behavioral and self-report domains. Multivariate Behav Res, 22(3), 357–379. doi:10.1207/s15327906mbr2203_6 Kreek, M.J., Nielsen, D. A., Butelman, E. R. & LaForge, K. S. (2005). Genetic influences on impulsivity, risk taking, stress responsivity and vulnerability to drug abuse and addiction. Nat Neurosci, 8(11), 1450–1457. doi:10.1038/nn1583 Moreno, M., Cardona, D., Gómez, M. J.,et al. (2010). Impulsivity characterization in the Roman high- and low-avoidance rat strains: behavioral and neurochemical differences. Neuropsychopharmacology, 35(5), 1198–208. doi:10.1038/npp.2009.224 Poulos, C. X., Le, A. D. & Parker, J. L. (1995). Impulsivity predicts individual susceptibility to high levels of alcohol self-administration. Behav Pharmacol, 6(8), 810–814. doi:10.1097/00008877-199512000- 00006 Robinson, E. S., Eagle, D. M., Mar, A. C.,et al. (2008). Similar effects of the selective noradrenaline reuptake inhibitor atomoxetine on three distinct forms of impulsivity in the rat.Neuropsychopharmacology, 33(5), 1028–1037. doi:10.1038/sj.npp.1301487 Rodriguez-Cintas, L., Daigre, C., Grau-López, L.,et al. (2016). Impulsivity and addiction severity in cocaine and opioid dependent patients. Addict Behav, 58, 104–109. doi:10.1016/j.addbeh.2016.02.029 Seger, C. A. & Spiering, B. J. (2011). A critical review of habit learning and the basal ganglia. Front Syst Neurosci, 5, 66. doi:10.3389/ fnsys.2011.00066 References 129

/ CH A PTER N I N E Impacts of Brain-Based Discoveries on Prevention and Intervention Approaches Learning Objectives • Be able to understand how addiction is a chronic brain disease. • Be familiar with pharmacological targets for addiction. • Be able to describe the cognitive mechanisms supported by behavioral treatment. • Be able to characterize the synergy between pharmacological and behavioral approaches. • Be able to identify the biological pathways targeted by interventions. Introduction Because the effects of addiction have such high social implications, it has historically been viewed primarily as a social problem (i.e. “disordered will”) rather than a medical/health problem. This misconception has contributed to the current lack of successful approaches to the prevention and intervention of addiction. Over the last two decades, and partly due to the “Decade of the Brain” in 1990–2000, a greater scientific understanding and public awareness of addiction as a chronic brain disease emerged. Thus, current effective treatment programs are based on the understanding that addiction is a treatable disease that affects brain function, and that treatment must be individualized and address other possible mental disorders. As discussed in Chapter 5, although drugs of abuse have different mechanisms of action, neuroscientific research, particularly in vivo human neuroimaging studies, has provided evidence that they all alter the brain’s dopaminergic signaling in the mesolimbic reward system. Dysfunction in this system leads to alterations in reward-processing, motivational and goal-directed behaviors as well as inhibitory control, as discussed throughout this book. These are therefore key brain regions and processes that can be targeted in therapeutic interventions.

/ Addiction is a lifelong, chronic brain disease. The term chronic reflects its enduring pathology, which suggests a high likelihood that symptoms of addiction will recur despite abstinence from the substance (i.e. relapse). To put this into perspective, the rate of relapse for addiction is similar to that of other chronic diseases such as diabetes, hypertension and asthma, all of which have physiological as well as psychological components, and have the same rate of medication adherence (Figure 9.1). Current intervention strategies focus on supporting abstinence by alleviating withdrawal symptoms, promoting treatment adherence and supporting protracted abstinence through prevention of relapse. There are several treatment approaches including pharmacological as well as behavioral/neurocognitive methods. Research shows that a combination of approaches facilitates greater outcomes, which is in line with the high complexity of addiction and the recovery process. Indeed, treatment strategies must take into account that the disruptions caused by addiction are widespread, affecting, among others, medical, psychological, social and occupational aspects of the individual. Thus, treatment programs incorporate comprehensive rehabilitation services to meet these varied needs (Figure 9.2). See Spotlight 1 for a description of the socio-occupational support provided by peer counseling programs. So, how has current scientific knowledge of addiction as a brain disorder been translated into clinical applications that benefit those who need it most? What are novel entry points that can be exploited 80 70 60 50 40 30 20 10 Type 1 diabetes Relapse rates at 1 year post-discharge Hypertension Asthma Alcohol dependence 0 Figure 9.1 Relapse rates for drug-addicted patients compared with those suffering from diabetes, hypertension and asthma. Relapse is common and similar across these illnesses (as is adherence to medication). Thus, drug addiction should be treated like any other chronic illness, with relapse serving as a trigger for renewed intervention. (Data from McLellanet al., 2000.). Introduction 131

/ for the development of more effective treatment? Exciting progress in neuroscience research is in the translation of these neuroimaging findings into clinical applications that promise to improve the status quo of clinical practice. A typical drug treatment protocol involves several steps, including: 1) detoxification (the process by which the body rids itself of a drug); 2) initial recovery where the focus is on sustaining motivation; and 3) relapse prevention, which may include treatment for co-occurring mental health issues such as depression and anxiety. This chapter will focus on how neuroscience research has advanced our informed addiction prevention and intervention strategies. Translational neuroscience research has: 1) advanced our understanding of risk factors that could facilitate early intervention; 2) facilitated improvement of standard treatment programs; 3) provided information on who, what and how intervention will be effective; and 4) fostered the development of novel and more targeted interventions. Pharmacological Approaches Pharmacological interventions are an important part of treatment, especially when combined with behavioral therapies. Medications can be Assessment Evidence-Based treatment Substance use monitoring Clinical/case management Recovery support programs Continuing care Vocational services Mental health services Medical services Educational services HIV/AIDS services Legal services Family services Figure 9.2 Components of comprehensive drug addiction treatment. The best treatment programs provide a combination of therapies and other services to meet the needs of the individual patient. (From National Institute on Drug Abuse, 2018.) 132 Impacts of Brain-Based Discoveries

/ used to manage withdrawal symptoms, prevent relapse and treat cooccurring conditions by targeting specific receptors, either activating or blocking their mechanism of action, thereby interrupting how substances of abuse interact with brain receptors. There are a number of pharmacotherapies currently used for treatment of opioid, tobacco and alcohol addiction. Studies are underway to develop similar pharmacotherapies for stimulant and cannabis addiction. Opioid receptor medications include both opioid receptor agonists and antagonists. Currently, methadone and buprenorphine are the only opioid agonists approved for drug treatment in the USA (see Spotlight 2 to understand how legislation balances the costs related to opioid addiction). Opioid agonist therapy is effective in managing opioid withdrawal and in reducing craving. Methadone, specifically, is a μ-opioid agonist as well as an N-methyl-d-aspartate (NMDA) receptor antagonist. Functional magnetic resonance imaging (fMRI) studies show that reductions in craving as a result of methadone treatment are associated with decreased activation in the limbic system (Li et al., 2013). Mass spectrometry imaging studies confirm that methadone is distributed in the striatal and hippocampal regions, including the nucleus caudate, putamen and upper cortex in in vivo rat brains (Teklezgi et al., 2018). These findings suggest that mitigation of cue-induced craving may be the primary effect of methadone that may be key in long-term abstinence (Figure 9.3) (Li et al., 2013). The NMDA antagonist effect involves modulation of the glutamatergic system, which is thought to mediate the development of tolerance. Naltrexone is aμ-opioid, κ-opioid and δopioid antagonist and is approved for the treatment of opioid and alcohol use disorder. Studies show that naltrexone leads to good outcomes in decreasing subjective craving, which has been associated with decreases in the neural response to alcohol cues during fMRI in orbital and cingulate gyri, and inferior frontal and middle frontal gyri – areas important for emotion, cognition, reward, punishment and learning/ memory. This attenuation of salience of alcohol cues may be the primary mechanism for the prevention of relapse. Cholinergic medications modulate the cholinergic system and are used primarily during tobacco smoking cessation. Bupropion is a nicotinic acetylcholine receptor (nAChR) antagonist and inhibits neuronal reuptake of dopamine. In effect, bupropion reduces craving. In contrast, varenicline is a partial agonist of the α4β2 subtype and full agonist of the α7 nAChR subtype, therefore leading to enhancement of cholinergic transmission. Studies have shown that it reduces nicotine withdrawal symptoms and improves cognitive performance through increased activation of the prefrontal cortex (PFC) (Loughead et al., 2010). Because of Pharmacological Approaches 133

/ the cognitive-enhancing effects of nAChR agonists, these medications have also been examined for the improvement of cognitive impairment in other types of addiction. For example, galantamine is an acetylcholinesterase inhibitor as well as an allosteric potentiator of the nAChR, and has been found to improve cognitive performance – sustained attention and working memory function – contributing toward decreased drug use (tested via a urine screen) in cocaine users (Sofuoglu & Carroll, 2011). Studies comparing bupropion with varenicline have reported greater rates of cessation with varenicline at 3 and 12 months post-detoxification, which highlights the important role of cognitive functioning in promoting behaviors necessary to maintain abstinence (Johnson, 2010). Similarly, the combination therapy of varenicline and bupropion yields greater efficacy than monotherapy (Vogeler et al., 2016). Acamprosate has a chemical structure similar to that ofγ-aminobutyric acid (GABA) and acts primarily by restoring normal NMDA receptor tone in the glutamate system. Acamprosate is thought to also suppress excitation-induced calcium entry that results from chronic alcohol exposure, thereby altering the conformation of the NMDA -12R L -9 -6 +3 +6 +9 +12 +15 +18 +21 +24 +39 +42 +45 +48 -5.00 T value -3.20 Figure 9.3 Following methadone-assisted therapy (MAT), long-term abstinent heroin users (mean length of abstinence, 193 days) had a greater decreased response in striatal areas compared with short-term abstinent heroin users (mean length of abstinence, 23 days) during a cue-induced craving task. (From Liet al., 2013.) (A black and white version of this figure will appear in some formats. For the color version, please refer to the plate section.) 134 Impacts of Brain-Based Discoveries

/ receptors. The balance of GABA and glutamate tone may be the mechanism that leads to its therapeutic effects. Acamprosate has been shown to reduce craving, leading to dose-dependent effects on decreasing alcohol consumption, increasing rate of treatment completion and maintaining abstinence. Using magnetoencephalography (see Chapter 2) in alcohol-dependent participants, it was found that acamprosate decreased the arousal level during alcohol withdrawal, as indicated by α slow-wave index measurement, in the parietotemporal regions (Boeijinga et al., 2004). This finding is in line with the notion that acamprosate modulates neuronal hyperexcitability of acute alcohol withdrawal, acting through glutamatergic neurotransmission. The aldehyde dehydrogenase inhibitor disulfiram is an alcoholaversive agent that has also been used to treat alcohol use disorder as a deterrent. Disulfiram markedly alters the metabolism of alcohol, which leads to increased blood acetaldehyde concentrations. This accumulation of acetaldehyde leads to aversive effects such asflushing, systemic vasodilation, respiratory difficulties, nausea, hypotension and other symptoms (i.e. acetaldehyde syndrome). In contrast to anti-craving medications, disulfiram does not modulate neurobiological reward mechanisms but rather works by producing an aversive reaction to alcohol. As a deterrent, the therapeutic effect of disulfiram in supporting abstinence is mediated through its psychological effects, i.e. the expectancy effect due to anticipation of the aversive reaction. Evidence for this comes from a meta-analysis, which showed that the significant therapeutic effects of disulfiram are greater in open-label trials (Skinneret al., 2014). Behavioral Approaches Behavioral approaches are designed to enhance the cognitive deficits linked to addiction, particularly prefrontal lobe functioning. Prefrontal areas such as the orbitofrontal, dorsolateral prefrontal and anterior cingulate cortices mediate executive functioning such as attention, working memory, decision making, set shifting and inhibitory control, among others. Cognitive behavioral models provide cognitive strategies and training that increase self-control and awareness of triggers for drug use. For example, cognitive behavioral therapy (CBT) may be utilized for the reduction of a cue-elicited craving response. The “active ingredients” of CBT may exert their effects via strengthening aspects of executive control over behavior. Although the neural mechanisms by which CBT exerts its therapeutic effects are still unclear, neuroimaging studies have begun to understand that improvement of brain network function is Behavioral Approaches 135

/ involved. For example, CBT has been shown to strengthen the network connectivity that underlies executive functioning, such as attention (Lewis et al., 2009). Additionally, an fMRI study investigating cueinduced craving and using instructions based on CBT strategies to focus on long-term consequences of tobacco use rather than short-term pleasurable tobacco associations found that dorsolateral PFC regions exerted control over ventral striatal activation in the regulation of craving (Kober et al., 2010). Cognitive rehabilitation strategies provide intensive exposure to computerized exercises that strengthen memory, attention, planning and other executive functioning. Improvement of these cognitive skills should therefore result in: 1) greater cognitive control over learned behavior related to substance use; 2) decreased impulsivity; 3) improved decision making; and 4) awareness of cognitions associated with drug use. Neuroimaging studies suggest that cognitive rehabilitation may normalize regional brain activation in the PFC (Wexler et al., 2000). Bickel et al. (2011) demonstrated that focused training on computerized memory tasks resulted in significant reductions in an aspect of impulsivity, delay discounting (i.e. preference for immediate versus delayed rewards), among stimulant users. Psychosocial interventions such as motivational enhancement therapy (MET) and motivational interviewing (MI) are brief and focused interventions that aim to increase one’s motivation to change. Research suggests that the efficacy of these approaches depends on age, type of drug addiction and the goal of the intervention. For example, MET has shown treatment success in cannabis-using adults but not consistently in adolescents or in those using cocaine, heroin or nicotine. Feldstein Ewing et al. (2011) suggested that MI supports a reduction in substance use by attenuation of the response in regions in the reward pathway, which suggest that the efficacy of MI is in reducing the salience of drug cues. Furthermore, they found that the active ingredient in MI, i.e. client change talk, elicited activation in areas that underlie self-awareness– the left inferior frontal gyrus/anterior insula and superior temporal gyri (Feldstein Ewing et al., 2014). Contingency management (CM) approaches have shown strong empirical support in randomized clinical trials. CM corrects the amplified valuation of immediate reward and the discounted value of delayed rewards (delay discounting) by reinforcing targeted outcomes with positive incentives. Delay discounting has been associated with poor treatment outcome for addiction and has been shown to involve cortical and subcortical systems involved in decision making (Balleine et al., 2007). Subcortical reward regions such as the 136 Impacts of Brain-Based Discoveries

/ ventral striatum are highly sensitive to small immediate rewards, whereas cortical regions in the PFC are more engaged during larger but delayed rewards (Kable & Glimcher, 2007). Combined Approaches The theory behind combined approaches is that neural alterations induced by pharmacotherapy may complement the cognitive mechanisms that behavioral approaches target. For instance, the reduced sensitivity to drug cues obtained by an anti-craving medication could be augmented by better cognitive control skills developed through CBT. Such a combined approach would maximize treatment success, particularly if implemented in early recovery when these skills are still developing. There is evidence to support the notion that combined pharmacological and behavioral therapies lead to better treatment outcomes than monotherapies. In one example, bupropion together with group counseling in nicotine users showed a reduction in glucose metabolism in the posterior cingulate cortex, an important region for goaldirected behavior, relative to monotherapy (Costelloet al., 2010). Sofuoglu et al. (2013) combined galantamine and CBT intervention to leverage the enhancing benefits of galantamine for improved memory and attention, which could then facilitate learning of CBT skills and strategies. Combined treatment boosts the efficacy of each individual approach, especially during a critical period when the greatest opportunities for improvements can be made (i.e. early recovery). These studies suggest that synergistic mechanisms occur in pharmacological and behavioral therapies. Potenza et al. (2011) proposed a model by which brain mechanisms may mediate the effects of combined behavioral and pharmacological treatments for the treatment of addiction (Figure 9.4). They proposed that behavioral approaches are more efficacious in targeting “top-down” PFC functions, such as inhibitory control, whereas pharmacological treatments are more targeted toward subcortical or“bottomup” processes, such as the reward–craving response. Konova et al. (2013) reviewed the neuroimaging literature on the brain response to addiction interventions to determine the mechanisms by which these distinct interventions work independently and synergistically. Specifically, using a meta-analysis, they examined the distinct and common neural patterns associated with pharmacological and behavioral monotherapies. Overall, they found significant overlaps in the mechanisms between pharmacological and behavioral approaches in the dopaminergic reward pathway, i.e. the ventral striatum, inferior frontal gyrus Combined Approaches 137

/ and orbitofrontal cortex (Figure 9.5). They also noted that, while there were overlaps, behavioral interventions were more likely to modulate the response in the anterior cingulate, middle frontal gyrus and precuneus/posterior cingulate cortex relative to pharmacological interventions, confirming the “top-down” notion of behavioral interventions as suggested by the model of Potenza et al. (2011). Overall, thesefindings suggest a potential mechanism by which the combined use of pharmacological and cognitive-based strategies may produce synergistic (due to their common targets) or complementary (due to their distinct targets) therapeutic effects. The influences of behavioral interventions on prefrontal and parietal cortical regions may be important for treatment adherence. Treatment Outcomes To date, prognosis following treatment is difficult to assess given the lack of knowledge with regard to the extent to which cognitive and neurobiological impairments recover with abstinence. As mentioned earlier, recovery is complex, and improvements do not Prefrontal cortex Executive functions, response inhibition to drug cues, inhibition of drug-seeking behavior Cognitive enhancement treatments Partial nAChR agonists α2 agonists and NET inhibitors L. Cer. NE neurons Drug withdrawal NE DA Nac Drug reward Dysphoria Glutamate medications DAT inhibitors Behavioral treatments (CBT, CM, MI and other) DA agonists GABA and antagonists medications Opioid agonist and antagonists DA Glu VTA DA neurons Figure 9.4 Proposed model illustrating synergistic mechanisms between behavioral and pharmacological treatment approaches for addiction. DA, dopamine; DAT, dopamine transporter; Nac, nucleus accumbens; Glu, glutamate; VTA, ventral tegmental area; L. Cer., locus coeruleus; NE, norepinephrine; NET, norepinephrine transporter. (From Potenza et al., 2011. © 2011 Elsevier, USA.) 138 Impacts of Brain-Based Discoveries

/ (a) R P P R Pharmacological interventions Y=13 MFG MFG MFG MFG IFG IFG IFG Prec Prec ACC ACC Prec OFC OFC OFC VS VS MFG MFG VS Y=23 X=–3 X=8 Z=40 Cognitive-based interventions Conjunction A A L (b) L Pharmacological interventions Cognitive-based interventions Cognitive-based > pharmacological MFG MFG Prec Prec Figure 9.5 Common (a) and distinct (b) neural targets of pharmacological and cognitivebased therapeutic interventions. Threshold for conjunction:P<0.005 uncorrected and a minimum cluster size of 100 mm3 . Threshold for difference contrast:P<0.05 false discovery rate-corrected and a minimum cluster size of 100 mm3 . A, anterior; ACC, anterior cingulate cortex; IFG, inferior frontal gyrus; L, left; MFG, middle frontal gyrus; OFC, orbitofrontal cortex; P, posterior; Prec, precuneus; R, right; VS, ventral striatum. (From Konovaet al., 2013. © 2013 Elsevier, USA.) (A black and white version of thisfigure will appear in some formats. For the color version, please refer to the plate section.) Treatment Outcomes 139

/ have a clear, linear relationship with the duration of abstinence. For example, underactivation of the inhibitory control network may worsen during the early stages of withdrawal before it rebounds during protracted abstinence. This makes timing of treatment strategies, such as bolstering inhibitory control, critical, given that weakness in prefrontal control systems during early withdrawal poses a high risk for relapse. In general, cognitive impairments are associated with poorer adherence to treatment. For example, cocaine users who failed to complete CBT had significantly worse performance on tests of attention, memory, spatial ability, speed, accuracy, global functioning and cognitive proficiency compared with those who completed the CBT regimen (Aharonovich et al., 2006). Similar findings were found in cannabis users who did not complete treatment, i.e. poorer abstract reasoning and processing accuracy (Aharonovich et al., 2008). In addition to cognitive performance predicting treatment adherence, performance on measures of risk taking and sustained attention has been found to predict CBT outcomes in terms of negative drug screens in cocaine users. Notably, overall cognitive performances as indexed by a composite score did not predict treatment response, suggesting a specificity of the effects of cognitive domains on the clinical course of drug treatment outcomes (Carroll et al., 2011). In general, impairments in inhibitory control tend to be associated with poorer outcomes (Verdejo-Garcia et al., 2012). Long-term relapse prevention is the biggest challenge in addiction intervention. Studies only show modest effect sizes of current approaches because of the heterogeneity of patient samples. Given the individual variability of addiction in terms of risks and manifestations, “one size does not fit all.” Identifying effective treatment has shown promise when biologically defined endophenotypes (versus behavioral symptoms) are used. For example, naltrexone treatment has been found to be more effective in carriers of a specific variant of the μ-opioid receptor gene (Chen et al., 2013). Similar genetic effects may be present for the response to acamprosate, specifically in genes associated with glutamatergic/GABAergic negative reinforcement system (Ooteman et al., 2009). Very recently, biological differences between patient groups are also being identified using functional neuroimaging. Naltrexone is suggested to work better in a subgroup of patients with higher cue reactivity when shown appetitive alcohol pictures. Magnetic resonance spectroscopy of brain glutamate levels may detect potential acamprosate responders. 140 Impacts of Brain-Based Discoveries

/ Summary Points • Studies demonstrate that a combination of pharmacological and cognitive approaches lead to better treatment success. • There are three stages to the recovery from addiction: detoxification, initial recovery and relapse prevention. • The synergistic mechanisms in combined pharmacological and behavioral therapies may be a combination of “top-down” mechanisms through behavioral intervention with “bottom-up” processes in pharmacological approaches. Review Questions • What are the common targets of pharmacological and cognitive therapies? • How can neuroimaging methods lead to individualized treatment? • What are the three primary stages of addiction intervention? • How could behavioral and pharmacological treatment mechanisms complement each other? • What biological pathways do behavioral and pharmacological treatments both target? Further Reading Bickel, W. K., Christensen, D. R. & Marsch, L. A. (2011). A review of computer-based interventions used in the assessment, treatment, and research of drug addiction. Subst Use Misuse, 46(1), 4–9. doi:10.3109/ 10826084.2011.521066 Chung, T., Noronha, A., Carroll, K. M.,et al. (2016). Brain mechanisms of change in addictions treatment: models, methods, and emergingfindings. Curr Addict Rep, 3(3), 332–342. doi:10.1007/s40429-016-0113-z Feldstein Ewing, S. W., Filbey, F. M., Hendershot, C. S., McEachern, A. D. & Hutchison, K. E. (2011). Proposed model of the neurobiological mechanisms underlying psychosocial alcohol interventions: the example of motivational interviewing.J Stud Alcohol Drugs, 72(6), 903–916. Feldstein Ewing, S. W., Filbey, F. M., Sabbineni, A., Chandler, L. D. & Hutchison, K. E. (2011). How psychosocial alcohol interventions work: a preliminary look at what FMRI can tell us. Alcohol Clin Exp Res, 35(4), 643–651. doi:10.1111/j.1530-0277.2010.01382.x Further Reading 141

/ Feldstein Ewing, S. W., Houck, J. M., Yezhuvath, U.,et al. (2016). The impact of therapists’ words on the adolescent brain: in the context of addiction treatment. Behav Brain Res, 297, 359–369. doi:10.1016/j.bbr.2015.09.041 Feldstein Ewing, S. W., McEachern, A. D., Yezhuvath, U.,et al. (2013). Integrating brain and behavior: evaluating adolescents’ response to a cannabis intervention. Psychol Addict Behav, 27(2), 510–525. doi:10.1037/ a0029767 Gilfillan, K. V., Dannatt, L., Stein, D. J. & Vythilingum, B. (2018). Heroin detoxification during pregnancy: a systematic review and retrospective study of the management of heroin addiction in pregnancy.S Afr Med J, 108(2), 111–117. doi:10.7196/SAMJ.2017.v108i2.7801 Glasner-Edwards, S. & Rawson, R. (2010). Evidence-based practices in addiction treatment: review and recommendations for public policy. Health Policy, 97(2–3), 93–104. doi:10.1016/j.healthpol.2010.05.013 Gorsane, M. A., Kebir, O., Hache, G.,et al. (2012). Is baclofen a revolutionary medication in alcohol addiction management? Review and recent updates. Subst Abus, 33(4), 336–349. doi:10.1080/08897077.2012.663326 Liu, J., Nie, J. & Wang, Y. (2017). Effects of group counseling programs, cognitive behavioral therapy, and sports intervention on internet addiction in East Asia: a systematic review and meta-analysis. Int J Environ Res Public Health, 14(12). doi:10.3390/ijerph14121470 Spotlight 1 Leveraging the Power of Peer Influence The astounding rise in rates of addiction in the USA has led to a high need for addiction treatment specialists. Some areas such as Lehigh Valley in Pennsylvania have addressed this rising rate of addiction by relying on certified recovery specialists. Certified recovery specialists are individuals who themselves are in long-term recovery from addiction. After completion of over 50 h of intensive training related to recovery management, certified recovery specialists can then help others in need by providing support in a similar way to their own recovery. Pennsylvania’s training program was established in 2008, and today, peer counseling programs exist nationwide in the USA. Peer recovery specialists support clients’ recovery from addiction alongside healthcare specialists who provide the necessary treatment. Peer recovery specialists leverage their own experience living in recovery and assist clients during the transition from treatment back to society (Figure S9.1). They guide on practical matters such as finding employment, housing and education. 142 Impacts of Brain-Based Discoveries

/ In the case of Lehigh Valley, each peer recovery specialist supports up to thirty clients. The benefits of peer counseling programs are reciprocal. The process of providing support and managing the functional needs of others encourages peer recovery specialists to maintain the same level of expectations for themselves. In short, as peer counselors encourage their clients to resist the urge to use substances, so do they. Witnessing others overcome their addiction through the program also keeps the peer counselors motivated and encouraged to continue down their path. Spotlight 2 The Balance of Legislation and Cost of Addiction Treatment The US Department of Health and Human Services estimated that, in 2015, the opioid epidemic cost $55 billion in health and social services and $20 billion in emergency department and inpatient care for opioid poisonings. Given the upward trend in rates of opioid-related deaths in the USA (e.g. 8% Figure S9.1 Peer addiction recovery specialists bring different perspective to treatment. Spotlight 2 143

/ in 2010 to 25% in 2015, according to the Centers for Disease Control and Prevention), the costs for treatment programs are expected to rise, contributing toward growing economic challenges in healthcare. For example, the budget cuts in the Affordable Care Act’s requirement for addiction services under Medicaid have led to a 2018 ban on drug toxicology tests that verify adherence to treatment and abstinence during addiction treatment in Maryland. The Maryland Medicaid program claimed to have spent 23% of its $315 million budget for substance use treatment. Most legislators acknowledge the opioid epidemic and advocate for more drug treatment centers but are hindered by the associated costs. As an alternative approach, legislative leaders, such as those in Indiana, have reached out to private foundations to help fund more centers. Additionally, a Senate committee is considering a bill that allows tougher penalties against drug dealers if one of their customers dies of an overdose. Despite these costs, changes in legislation have been put in place to maximize treatment opportunities. In 2017, Jessie’s Law was passed by the Senate ensuring that clinical providers have information on patients’ substance abuse history1 . House-passed bills would make drug treatment available in jail to people charged with misdemeanors and would make it easier for drug counselors to be licensed, to fund overdose rescue medications such as naloxone and to study whether office-based treatment programs should be licensed. References Aharonovich, E., Hasin, D. S., Brooks, A. C.,et al. (2006). Cognitive deficits predict low treatment retention in cocaine dependent patients. Drug Alcohol Depend, 81(3), 313–322. doi:10.1016/j.drugalcdep.2005.08.003 Aharonovich, E., Brooks, A. C., Nunes, E. V. & Hasin, D. S. (2008). Cognitive deficits in marijuana users: effects on motivational enhancement therapy plus cognitive behavioral therapy treatment outcome. Drug Alcohol Depend, 95(3), 279–283. doi:10.1016/j. drugalcdep.2008.01.009 Balleine, B. W., Delgado, M. R. & Hikosaka, O. (2007). The role of the dorsal striatum in reward and decision-making. J Neurosci, 27(31), 8161–8165. doi:10.1523/JNEUROSCI.1554-07.2007 1 Jessie’s Law was named after Jessica Grubb who was in recovery from opioid abuse when she underwent surgery. Her discharging physician did not receive the information about her history of opioid use and erroneously discharged her with a prescription forfifty oxycodone tablets. Jessie overdosed and died the same night. 144 Impacts of Brain-Based Discoveries

/ Bickel, W. K., Yi, R., Landes, R. D., Hill, P. F. & Baxter, C. (2011). Remember the future: working memory training decreases delay discounting among stimulant addicts. Biol Psychiatry, 69(3), 260–265. doi:10.1016/j.biopsych.2010.08.017 Boeijinga, P. H., Parot, P., Soufflet, L., et al. (2004). Pharmacodynamic effects of acamprosate on markers of cerebral function in alcohol-dependent subjects administered as pretreatment and during alcohol abstinence. Neuropsychobiology, 50(1), 71–77. doi:10.1159/ 000077944 Carroll, K. M., Kiluk, B. D., Nich, C.,et al. (2011). Cognitive function and treatment response in a randomized clinical trial of computer-based training in cognitive-behavioral therapy. Subst Use Misuse, 46(1), 23–34. doi:10.3109/10826084.2011.521069 Chen, A. C., Morgenstern, J., Davis, C. M.,et al. (2013). Variation in muopioid receptor gene (OPRM1) as a moderator of naltrexone treatment to reduce heavy drinking in a high functioning cohort. J Alcohol Drug Depend, 1(1), 101. Costello, M. R., Mandelkern, M. A., Shoptaw, S.,et al. (2010). Effects of treatment for tobacco dependence on resting cerebral glucose metabolism. Neuropsychopharmacology, 35(3), 605–612. doi:10.1038/ npp.2009.165 Feldstein Ewing, S. W., Filbey, F. M., Sabbineni, A., Chandler, L. D. & Hutchison, K. E. (2011). How psychosocial alcohol interventions work: a preliminary look at what FMRI can tell us. Alcohol Clin Exp Res, 35(4), 643–651. doi:10.1111/j.1530-0277.2010.01382.x Feldstein Ewing, S. W., Yezhuvath, U., Houck, J. M. & Filbey, F. M. (2014). Brain-based origins of change language: a beginning. Addict Behav, 39(12), 1904–1910. doi:10.1016/j.addbeh.2014.07.035 Johnson, T. S. (2010). A brief review of pharmacotherapeutic treatment options in smoking cessation: bupropion versus varenicline.J Am Acad Nurse Pract, 22(10), 557–563. doi:10.1111/j.1745-7599.2010.00550.x Kable, J. W. & Glimcher, P. W. (2007). The neural correlates of subjective value during intertemporal choice. Nat Neurosci, 10(12), 1625–1633. doi:10.1038/nn2007 Kober, H., Kross, E. F., Mischel, W., Hart, C. L. & Ochsner, K. N. (2010). Regulation of craving by cognitive strategies in cigarette smokers. Drug Alcohol Depend, 106(1), 52–55. doi:10.1016/j. drugalcdep.2009.07.017 Konova, A. B., Moeller, S. J. & Goldstein, R. Z. (2013). Common and distinct neural targets of treatment: changing brain function in substance addiction. Neurosci Biobehav Rev, 37(10), 2806–2817. doi:10.1016/j.neubiorev.2013.10.002 References 145

/ Lewis, C. C., Simons, A. D., Silva, S. G.,et al. (2009). The role of readiness to change in response to treatment of adolescent depression. J Consult Clin Psychol, 77(3), 422–428. doi:10.1037/a0014154 Li, Q., Wang, Y., Zhang, Y., et al. (2013). Assessing cue-induced brain response as a function of abstinence duration in heroin-dependent individuals: an event-related fMRI study.PLoS One 8(5): e62911. doi:10.1371/journal.pone.0062911 Loughead, J., Ray, R., Wileyto, E. P., et al. (2010). Effects of theα4β2 partial agonist varenicline on brain activity and working memory in abstinent smokers. Biol Psychiatry, 67(8), 715–721. doi:10.1016/j. biopsych.2010.01.016 McLellan, A. T., Lewis, D. C., O’Brien, C. P. & Kleber, H. D. (2000). Drug dependence, a chronic medical illness: implications for treatment, insurance, and outcomes evaluation. JAMA, 284(13), 1689–1695. doi:10.1001/jama.284.13.1689 National Institute on Drug Abuse (2018). Treatment approaches for drug addiction. Available at: www.drugabuse.gov/publications/drugfacts/ treatment-approaches-drug-addiction (accessed November 11, 2018). Ooteman, W., Naassila, M., Koeter, M. W.,et al. (2009). Predicting the effect of naltrexone and acamprosate in alcohol-dependent patients using genetic indicators. Addict Biol, 14(3), 328–337. doi:10.1111/j.1369- 1600.2009.00159.x Potenza, M. N., Sofuoglu, M., Carroll, K. M. & Rounsaville, B. J. (2011). Neuroscience of behavioral and pharmacological treatments for addictions. Neuron, 69(4), 695–712. doi:10.1016/j.neuron.2011.02.009 Skinner, M. D., Lahmek, P., Pham, H. & Aubin, H. J. (2014). Disulfiram efficacy in the treatment of alcohol dependence: a meta-analysis. PLoS One, 9(2), e87366. doi:10.1371/journal.pone.0087366 Sofuoglu, M. & Carroll, K. M. (2011). Effects of galantamine on cocaine use in chronic cocaine users. Am J Addict, 20(3), 302–303. doi:10.1111/ j.1521-0391.2011.00130.x Sofuoglu, M., DeVito, E. E., Waters, A. J. & Carroll, K. M. (2013). Cognitive enhancement as a treatment for drug addictions.Neuropharmacology, 64, 452–463. doi:10.1016/j.neuropharm.2012.06.021 Teklezgi, B. G., Pamreddy, A., Baijnath, S., et al. (2018). Time-dependent regional brain distribution of methadone and naltrexone in the treatment of opioid addiction. Addict Biol, in press. doi:10.1111/ adb.12609 Verdejo-Garcia, A., Betanzos-Espinosa, P., Lozano, O. M.,et al. (2012). Selfregulation and treatment retention in cocaine dependent individuals: a longitudinal study. Drug Alcohol Depend, 122(1–2), 142–148. doi:10.1016/j.drugalcdep.2011.09.025 146 Impacts of Brain-Based Discoveries

/ Vogeler, T., McClain, C. & Evoy, K. E. (2016). Combination bupropion SR and varenicline for smoking cessation: a systematic review. Am J Drug Alcohol Abuse, 42(2), 129–139. doi:10.3109/00952990.2015.1117480 Wexler, B. E., Anderson, M., Fulbright, R. K. & Gore, J. C. (2000). Preliminary evidence of improved verbal working memory performance and normalization of task-related frontal lobe activation in schizophrenia following cognitive exercises. Am J Psychiatry, 157(10), 1694–1697. doi:10.1176/appi.ajp.157.10.1694 References 147

/ CH A P TER TE N Conclusions Learning Objectives • Be able to summarize how neuroscientific research has advanced our understanding of addiction. • Be able to appreciate how identifying risk factors can advance prediction and intervention strategies. • Be able to describe endophenotypes that lead to individual differences in susceptibility to psychoactive substances. • Be able to understand differences in manifestations of addiction across males and females. • Be able to explain the limitations and future needs of neuroscience research in addiction. Introduction As Chapters 1 and 9 discussed, the social implications of addiction have led to the stigma that addiction is a social problem. This general public opinion may originate from the more evident societal burden of addiction relative to the personal burden that is usually minimized by the sufferer. For example, approximately $67 billion is spent in the USA due to crime, lost work productivity and social support related to addiction. This stigma of addiction as a non-medical disorder has perpetuated in medical settings where the training curricula continue to place little to no emphasis on programs related to the treatment of addiction. As a result, medical practices rarely evaluate potential substance-related problems, which, in turn, leads to poor prognosis. The preceding chapters discussed how the operational definition of addiction has been validated by neuroscientific research in the absence of diagnostic laboratory tests or biomarkers for substance use disorder or addiction (see Chapter 1 for diagnostic criteria). Indeed, neuroscience research, especially with the advancements of in vivo human imaging techniques, has provided us

/ with knowledge of the neurobiological foundations for the observable symptoms of addiction. It has provided us with mechanisms by which we can develop effective treatment and make predictions of outcomes. In sum, neuroscientific research has shed light on the very complex neurobiological framework that parallels the complex behavioral sequelae of addiction. The neuroscience of addiction will continue to evolve as our understanding of these intricate neural processes deepens. Equally important will be an understanding of the interactions between these neurobiological processes and the myriad factors that modulate them. Not everyone who consumes drugs and alcohol becomes addicted. In fact, the prevalence of addiction relative to the total number of individuals who use drugs and alcohol is relatively modest. For example, among those who have tried cocaine, only about 17% become addicted; about 15% of those who drink become dependent; and for nicotine, 30% of those who try smoking become addicted smokers. What makes some individuals more vulnerable than others? What are the mechanisms that increase their brain’s sensitivity to the psychoactive effects of substances? Behavioral and genetic studies provide some information about these morbidities. The individual factors contributing to vulnerability to addiction are complex and have not yet been fully elucidated. This chapter will discuss neuroscientific discoveries on how these factors modulate the response to substances. Risk Factors Inform Better Prevention and Intervention Risk factors are defined as characteristics that heighten one’s likelihood for addiction. These factors could be biological, psychological, social or environmental. It is widely accepted that one of the primary risk factors associated with the development of addiction is adolescent onset of use. Developmental neuroscience studies posit that the rapid brain maturation of prefrontal network connections responsible for decision making and inhibitory control during adolescence makes the adolescent brain more vulnerable to the effects of psychoactive substances. Important neuromaturational processes during adolescence through to young adulthood are believed to bring about improved higher-order cognition by refining neural systems locally and globally through white and gray matter developments (Casey et al., 2005). In general, gray matter reductions and cortical thinning coincide with increased white matter volume and organization throughout adolescence and young adulthood, suggestive of synaptic pruning and axonal myelination (see Chapter 1) (Giorgio et al., 2010; Gogtay et al., 2004; Hasan et al., 2007; Lebel et al., Risk Factors Inform Better Prevention and Intervention 149

/ 2010; Shaw et al., 2008). Exposure to psychoactive substances during adolescence is thought to disrupt the strengthening of connections between higher-order association areas such as the corticostriatal network (Wierenga et al., 2016). Early life stress during this critical period for neurodevelopment has also been associated with a greater risk for later development of addiction. Stress induces the release of central corticotropin-releasing factor from the hypothalamus that binds to corticotropin-releasing factor receptors in the pituitary. This interaction in the pituitary stimulates the production of active peptides, including β-endorphin and adrenocorticotropic hormone, which is carried via blood to the adrenal glands where it induces the secretion of glucocorticoids. The glucocorticoids are then transported by the blood to the brain, where they act on numerous signaling systems including the dopaminergic reward system, in addition to systems involved in physiological stress responses (e.g. increases in blood glucose levels and blood pressure) (see Chapter 6 for more information on neuroadaptations related to stress). This stressrelated modulation of the reward system during neurodevelopment may therefore disrupt the maturational process of the reward system. Indeed, pre-clinical studies in rats show that early life stress is associated with dysregulation in midbrain circuitry (Chocyk et al., 2015), linked to dysfunctions in reward-related behavior (see Spotlight 1 for more on the interaction between stress and addiction). Addiction Endophenotypes There is strong evidence from family, adoption and twin studies of the role of genetic factors in the development of addictions (Ducci & Goldman, 2012). Figure 10.1 illustrates heritability across ten addictions, demonstrating that while almost all have at least 40% heritability, it is lowest for hallucinogens (39%) and highest for cocaine (72%). A specific area of neuroscientific research referred to as imaging genetics leverages knowledge from addiction genetics studies in order to determine the source of variability in neural signaling pathways associated with addiction. Specifically, genetic variability is associated with neurobiological processes gleaned from human in vivo neuroimaging methods, such as blood oxygenated level-dependent (BOLD) functional magnetic resonance imaging (fMRI), pharmacological fMRI and multimodal positron emission tomography (PET)/fMRI. A study by Hariri (2009) illustrated how linking genetic variability with the neurobiology of complex traits such as personality and temperament can identify individual variability 150 Conclusions

/ of risk, which can serve as an important predictor of vulnerability to addiction (Figure 10.2). In this example, the link between the genetic risk for depression (HTR1A-1019 G allele), whose functional significance is heightened serotonin signaling, and trait anxiety, which predicts depression, is amygdala reactivity. This link or intermediate expression between the genetic mechanism and the behavioral manifestation is referred to as an endophenotype. The concept of endophenotypes in psychiatric genetics was introduced by Gottesman and Shields (1972) to address the poor reproducibility of genetic findings and challenges in determining underlying etiologies based on diagnostic criteria in schizophrenia. They defined the concept as internal phenotypes that lie on the pathway between genes and disease and whose variation depend on variation in fewer genes than the more complex disease phenotype, as illustrated in Figure 10.3. In essence, endophenotypes should be more tractable to genetic analyses. Neuroscientific research has therefore focused on identification of endophenotypes that predispose individuals to compulsive drug use to allow 1 0.8 h 2 ± range Hallucinogens (4,570) Stimulants (2,212) Cannabis (7,659) Sedatives (4,758) Alcohol (9,897) Caffeine (6,997) Opiates (3,494) Cocaine (2,206) Gambling (3,359) Smoking (10,620) Addictive agents (number of twin pairs) Mean 0.6 0.4 0.2 0 Figure 10.1 Heritability (h 2 ; weighted means and ranges) of ten addictions based on a large survey of adult twins. (From Ducci & Goldman, 2012, adapted from Goldmanet al., 2005. © 2005 Springer Nature, USA.) Addiction Endophenotypes 151

/ Personality measure (e) Variability in measures of temperament and personality (e.g. trait anxiety) may predict risk for neuropsychiatric disease (e.g. depression), especially in the context of environmental stressors Variability in behaviorally relevant brain circuit function (e.g. threat-related amygdala reactivity) may represent a disease-related bias in processing specific types of information (e.g. attentional bias to threat) Variability in molecular signaling pathways (e.g. increased 5-HT1A autoreceptors assayed with PET) predicting this brain circuit function may represent a specific pathophysiological mechanism and therapeutic target (e.g. 5-HT1A autoreceptor antagonism) Functional genetic polymorphisms (e.g. HTR1A-1019G allele) efficiently represent emergent variability in the entire biological cascade from (c) to (a) and may represent predictive markers of specific disease processes that can lead to personalized medicine (e.g. administering 5-HT1A antagonists to only depressed patients possessing the1019 G allele) Observed distribution 20 30 40 50 60 (a) Brain circuit function Personality measure Brain circuit function Molecular signaling pathway Molecular signaling pathway Functional genetic polymorphism (b) (c) (d) 20 –0.5 0 30 40 50 60 0.5 1.0 1.5 –0.5 1.0 1.0 AA AB BB 2.0 3.0 4.0 5.0 6.0 7.0 2.0 0 0.5 1.0 1.5 3.0 4.0 5.0 6.0 7.0 Figure 10.2 Integration of complementary technologies (e) can be used to reveal the neurobiology of individual differences in complex behavioral traits. Speci fically, trait anxiety (a) associated with depression can be linked with amygdala reactivity (via fMRI) (b), which can then be associated with serotonin signaling (via PET) (c) and tied to variability in the HTR1A-1019 G allele (d). (From Hariri, 2009. © 2009 Annual Reviews, USA.) 152 Conclusions

/ better identification of genetic mechanisms and thus biological pathways, and to determine the functional consequences of risk-associated genes. As illustrated in Figure 10.4, Rangaswamy and Porjesz (2008) suggested that brain electroencephalography (EEG) oscillations are valuable endophenotypes for alcohol use disorders. Specifically, they found that θ (3–7 Hz) event-related oscillations underlying the P3 response are associated with individuals with alcohol use disorders and their unaffected relatives, and are linked with GABAergic, cholinergic and glutamatergic genes (GABRA2, CHRM2 and GRM8, respectively). These oscillations reflect a link between associative and integrative brain functions. Further associations between the inhibitory γ-aminobutyric acid (GABA) α2 receptor subunit (GABRA2) gene and alcohol use disorder have been reported using fMRI. Specifically, Villafuerte et al. (2011) found that increased activation in the insula cortex activation during anticipation of monetary rewards was correlated with impulsivity measures and the risk markers for alcohol use disorders. Brain structure may also be a useful endophenotype, as demonstrated by Schacht et al.(2012) (Figure 10.5). Their research showed an interaction between cannabinoid receptor 1 (CNR1) genes, hippocampal volume and cannabis use, whereby cannabis users with the risk genes (CNR1 G carriers) had smaller hippocampal volumes than controls. These endophenotypes can then be used to inform preventative approaches, which may include pro-social and cognitive support to Number of genes Complexity of phenotype and genetic analysis Figure 10.3 The concept of endophenotypes is that they lie in the causal pathway between the genetic mechanisms and observable behavior. (Redrawn by author, from Gottesman & Gould, 2003.) Addiction Endophenotypes 153

/ develop decision making and inhibitory control process that would lead to better avoidance of risk-taking behavior. These strategies may be particularly useful in high-risk individuals, such as adolescents with a family history of addiction, peer drug influences, externalizing and risk- exon exon exon exon exon 12 Controls (N=100) EROTOT 0 0 700 0 3.5 2.5 1.5 0.5 LOD 2 1 0 3 0 20 20 10 30 40 40 60 0 20 40 60 Power µv2 Power µv 2 Head plot power µv2 Alcoholics (N=100) EROTOT 12 Brain oscillations Fz 0 700 0 Chromosome 7 Cz, Max LOD=3.6 at 164 cM Pz, Max LOD=2.29 at 162 cM Fz, Max LOD=3.16 at 161 cM θ θ 0D7S1790 D7S513 D7S1802 D7S629 D7S673 D7S1838 NPY2 D7S817 D7S2846 D7S521 D7S691 D7S478 D7S679 D7S665 D7S1830 D7S3046 D7S1870 D7S1797 D7S820 D7S821 D7S1796 D7S1799 D7S1817 D7S2847 D7S498 D7S1889 D7S1804 D7S509 D7S1824 D7S794 D7S1805 rs1424558 rs1424558 rs1424574 rs1424569 rs1424387 rs2350780 rs978437 cc785 cc1218 rs7782965 rs7800170 rs1455858 rs1378646 rs1824024 rs2061174 rs7799047 rs2350786 chrm2ex5 rs6948054 rs324640 rs324650 rs324651 rs8191992 rs8191993 rs1378650 rs1424548 rs324656 rs13247260 downsteam exon6 upstream exon1 intron3-4 intron4-5 intron5-6 exon5 3’UTR 1 2 SNPs 3 81.7 kb 41.1 kb 22.6 kb 4 5 6 Coding Sequence 5’ -UTR 3’ -UTR 20 40 60 80 100 120 140 160 180 CHRM2 Candidate gene Genetic linkage GRM8 CHRM2 Genetic association Chromosome position (cM) θ Fz Figure 10.4 Brain EEG oscillations may be useful endophenotypes for alcohol use disorders. (From Rangaswamy & Porjesz, 2008.) (A black and white version of thisfigure will appear in some formats. For the color version, please refer to the plate section.) 154 Conclusions

/ taking behaviors, psychiatric disorders, etc. In terms of treatment, risk factors could exacerbate the symptoms of addiction; thus, treatment approaches should place emphasis on identifying and managing these vulnerability mechanisms. Comprehensive cognitive assessments help identify significant cognitive impairments from risk factors that compound the presentation of addiction. Knowing each individual ’s cognitive profile could better facilitate targeted strategies that support treatment in those with specific risk factors. Sex Differences in Addiction There is an emergent need to better understand the mechanisms by which the response to substances might differ between males and females. Understanding these differences can help to provide more effective treatment, as well as develop treatments that could modulate *** *** ** Volume (mm3) L hippocampus* R hippocampus* 5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0 Controls A/A Controls A/A and G/G Cannabis A/A and G/G Cannabis A/A Figure 10.5 Changes in brain volume may be an endophenotype for cannabis use disorder. The graph shows a significant difference in bilateral hippocampal volumes for cannabis users and matched healthy controls according to genotype. *P±0.05 for interaction between group and genotype; **P±0.05; ***P±0.001. L, left; R, right. (From Schacht et al., 2012.) Sex Differences in Addiction 155

/ the effects of hormones on treatment outcomes. Behaviorally, there are sex differences in terms of the development of addiction where females escalate more quickly and experience greater withdrawal symptoms than males. For example, female rats develop conditioned place preference at a lower threshold than males and are much more responsive to drugconditioned stimuli. Sex effects have also been observed in brain function. fMRI during cue reactivity showed a greater response to cues in the striatum, hippocampus, amygdala and lateral orbitofrontal cortex in females than in males (Wetherill et al., 2015). These results highlight differential reward processing in males and females. Beyond sex differences, the impact of hormones on the response to substances has also been noted. In women, subjective feelings fluctuate during the menstrual cycle whereby a greater response to drugs (e.g. cocaine) has been observed during the follicular phase but is reduced during the luteal phase. Pre-clinical studies have also shown greater reinstatement as a function of estradiol levels but are attenuated by progesterone. The estrous cycle also influences the effects of stimulants on psychomotor behavior (Bobzean et al., 2014). Research has suggested that the primary mechanism for sex differences in addiction is likely due to the interaction between hormone and dopamine function. First, there are basal differences. Females are reported to have lower levels of dopamine than males, which is likely to contribute to greater impulsivity and vulnerability toward addiction. Males have up to 10% more striatal dopamine receptors than females and have more dopamine release in the striatum relative to females. There is also sexual dimorphism on the effects of estradiol. Estradiol directly stimulates dopamine release in the striatum, but estradiol downregulates dopamine receptor D2 binding in females but not in males. The Question of Causality An important question in terms of the neuroscience of addiction is whether neural abnormalities are precursors to addiction that place individuals at heightened vulnerability to the effects of substances, or are the direct effects of substances on the brain. To address this important question, studies ideally should evaluate these key brain processes before and after exposure to substances. However, such studies are difficult and expensive. Thus, there are currently only a few longitudinal studies that we can draw from. One such study is the Dunedin Multidisciplinary Health and Development Study (often referred to as the Dunedin Longitudinal Study), which has been evaluating a long-standing 156 Conclusions

/ birth cohort of 1037 people born between April 1972 and March 1973 in Dunedin, New Zealand. The results of this study reported that daily cannabis users who initiated use during adolescence had elevated risk for psychosis as well as cognitive declines, such as a loss of 8 IQ points as assessed from age 11 to age 38 (Figure 10.6) (Meieret al., 2012). General Conclusions Neuroscientific research has advanced our knowledge of addiction as a brain disease by translating important findings from animal models of drug addiction in order to provide the foundations for studying the neurobiological basis of human drug addiction. These studies have Figure 10.6 (a) Birth cohort design. (b) The prospective study included initiation alcohol and drug use. (c) Using a prospective, longitudinal design on a birth cohort, the Dunedin General Conclusions 157

/ provided empirical evidence of the neurobiological framework to support concepts gleaned from behavioral studies. Neuroscientific research has provided multiple entry points for consideration in terms of prevention and intervention strategies through identification of the biological pathways that regulate the reward processes that underlie reward, motivation and inhibitory control. Neuroscienti fic research has also disentangled the processes that underlie the behavioral symptoms of craving and withdrawal. Through these studies, we have discovered the neuroadaptations that underlie the persistence of addiction and the wide brain One diagnosis Change in full-scale IQ (in standard deviation units) Cannabis dependent before age 18 (n =17) Cannabis dependent before age 18 (n =12) Not cannabis dependent before age 18 (n=57) Not cannabis dependent before age 18 (n =21) Cannabis dependent before age 18 (n =23) Not cannabis dependent before age 18 (n =14) Two diagnoses Three or more diagnoses 0.4 0.2 0 –0.2 –0.4 –0.6 –0.8 P = 0.44 P= 0.09 P = 0.02 (c) Figure 10.6 (cont.) Study found changes in full-scale IQ (in standard deviation units) from childhood to adulthood. Individuals who initiated cannabis use during adolescence (black bars) showed greater decrements in IQ relative to those who began use in adulthood (gray bars). (From: (b) https://pixabay.com/en/weed-smoke-drug-marijuana-joint-837125/; (c) Meieret al., 2012.) 158 Conclusions

/ networks implicated in these changes, particularly the mesocorticolimbic network, which is innervated by dopaminergic projections. These studies have also helped us understand the dynamic changes throughout the course of the addiction cycle that lead to the positive reinforcing effects of drugs and the negative reinforcing effects of drug abstinence. Interventions can be designed based on this neuroscientific knowledge so that specific brain pathways can be targeted and remediated by behavioral and pharmacological approaches that have been shown to be beneficial. Finally, through neuroscientific research, we are able to triangulate the events that occur between the genetic mechanisms and the expression of addiction to better understand factors that increase risk for, but also factors that might protect against, addiction. There is still a long road ahead as our understanding of these processes emphasizes the gaps in current knowledge. The Spotlight section throughout this book highlight some of these gaps that have current significance in society. See Spotlight 2 for an example of how advocacy can help change the face of and eliminate the stigma related to addiction. Summary Points • Advancements in neuroscience techniques have paved the way for our understanding of addiction as a brain disorder. • Neuroimaging techniques provide the ability to measure the electrophysiological, functional, structural and biochemical composition of the brain. • Brain imaging techniques provide evidence for associations between brain structure and function and behavioral symptoms of addiction. • Understanding neural mechanisms underlying behavioral symptoms of addiction is important in identifying potential targets for therapeutic interventions. • Dopamine dysregulation in substance abuse disorders is influenced by biological sex and hormone levels. Review Questions • How do risk factors leave the brain vulnerable to addiction? • What is the benefit of identifying endophenotypes for addiction? • What are the underlying mechanisms that underlie the difference in response to drugs between males and females? Review Questions 159

/ Further Reading Abasi, I. & Mohammadkhani, P. (2016). Family risk factors among women with addiction-related problems: an integrative review. Int J High Risk Behav Addict, 5(2), e27071. doi:10.5812/ijhrba.27071 Buckland, P. R. (2008). Will we everfind the genes for addiction?Addiction, 103(11), 1768–1776. doi:10.1111/j.1360-0443.2008.02285.x Ducci, F. & Goldman, D. (2008). Genetic approaches to addiction: genes and alcohol. Addiction, 103(9), 1414–1428. doi:10.1111/j.1360- 0443.2008.02203.x Feldstein Ewing, S. W., Filbey, F. M., Loughran, T. A., Chassin, L. & Piquero, A. R. (2015). Which matters most? Demographic, neuropsychological, personality, and situational factors in long-term marijuana and alcohol trajectories for justice-involved male youth. Psychol Addict Behav, 29(3), 603–612. doi:10.1037/adb0000076 Filbey, F. M., Schacht, J. P., Myers, U. S., Chavez, R. S. & Hutchison, K. E. (2010). Individual and additive effects of theCNR1 and FAAH genes on brain response to marijuana cues. Neuropsychopharmacology, 35(4), 967–975. doi:10.1038/npp.2009.200 Ketcherside, A., Baine, J. & Filbey, F. (2016). Sex effects of marijuana on brain structure and function. Curr Addict Rep, 3, 323–331. doi:10.1007/s40429- 016-0114-y Konova, A. B., Moeller, S. J., Parvaz, M. A.,et al. (2016). Converging effects of cocaine addiction and sex on neural responses to monetary rewards. Psychiatry Res, 248, 110–118. doi:10.1016/j.pscychresns.2016.01.001 McCrory, E. J. & Mayes, L. (2015). Understanding addiction as a developmental disorder: an argument for a developmentally informed multilevel approach. Curr Addict Rep, 2(4), 326–330. doi:10.1007/s40429-015-0079-2 Morrow, J. D. & Flagel, S. B. (2016). Neuroscience of resilience and vulnerability for addiction medicine: from genes to behavior.Prog Brain Res, 223, 3–18. doi:10.1016/bs.pbr.2015.09.004 Prashad, S., Milligan, A. L., Cousijn, J. & Filbey, F. M. (2017). Cross-cultural effects of cannabis use disorder: evidence to support a cultural neuroscience approach.Curr Addict Rep, 4(2), 100–109. doi:10.1007/s40429-017- 0145-z Puetz, V. B. & McCrory, E. (2015). Exploring the relationship between childhood maltreatment and addiction: a review of the neurocognitive evidence. Curr Addict Rep, 2(4), 318–325. doi:10.1007/s40429-015-0073-8 160 Conclusions

/ Spotlight 1 The Relationship Between Stress and Addiction Seamus McDonald was just 2.5 years old when he witnessed both of his parents being shot to death. This traumatic event not only changed his life instantly in that moment but also changed its course dramatically. McDonald was a responsible citizen and father; however, when he became involved with an organization that assisted victims of violence, the experience triggered the deeply rooted trauma from his early childhood. He began using cannabis to treat his post-traumatic stress disorder (PTSD) from the murder of his parents. The American Academy of Pediatrics now recognizes toxic stress as a mediating mechanism between behavioral problems and stress/trauma endured during childhood. Toxic stress leads to changes in multiple biological systems that contribute to vast alterations in behavioral and health problems in childhood and into adulthood, such as PTSD and addiction (Figure S10.1). Patients with PTSD have reported that cannabis provides relief from their symptoms with fewer side effects than prescribed medications. To date, most of what is known is based on anecdotal evidence. Research into the therapeutic effects of cannabis is hampered by US federal policies, especially the classification of cannabis as a Schedule I drug. For some researchers, these hurdles are worth overcoming so that much-needed questions can be answered. Figure S10.1 Post-traumatic stress disorder (PTSD). (From www.pexels.com/photo/adult-alone-black-and-white-dark-551588/.) Spotlight 1 161

/ Spotlight 2 A Rocker’s Fight Against Addiction In February 2018, the musician Flea disclosed his struggles with addiction in a Time editorial, “The temptation of drugs is a bitch” (http://time.com/ 5168435/flea-temptation-drug-addiction-opioid-crisis/). Flea, who is the lead bassist for the rock band Red Hot Chili Peppers, candidly described hisfirsthand life experiences that contributed to his substance abuse and addiction, and that eventually led him back to good health. Stating that drugs have been a fixture in his life since infancy, he also described witnessing loved ones’ lives end tragically due to addiction. He details how fulfilling responsibilities as a father was challenging yet influential in his fight against the disease and would later help him defeat it. Alongside his personal motivation, he ascribes his success to a number of support systems that included counseling, meditation, exercise and spiritual guidance. In the end, he claims that recognizing and accepting the challenges of addiction“helped [him] stay away from the temptation of drugs.” Alluding to the chronic nature of the disease, he adds, “It’s always there, seducing you to come on in and get your head right,” as he describes repeatedly dealing with severe anxieties that challenge his sobriety. In light of the current opioid epidemic in the USA, he recalls his own experience with opioids and is forthright about the role that the medical community played in this crisis (see Spotlight sections in Chapter 9 to learn how legislation is addressing the opioid crisis). He cited that, following a broken arm, his physician overprescribed oxycodone (OxyContin), sending him home with a 2-month supply with instructions to take as many as four pills per day. He described how Oxycontin removed his physical pain but also diminished his ability to function personally and professionally. Although Flea discontinued his use of Oxycontin before his 2-month supply was depleted, his first-hand experience has given him insight into how little we know about pain management and how our current approaches need to be improved. References Bobzean, S. A., DeNobrega, A. K. & Perrotti, L. I. (2014). Sex differences in the neurobiology of drug addiction. Exp Neurol, 259, 64–74. doi:10.1016/j.expneurol.2014.01.022 Casey, B. J., Tottenham, N., Liston, C. & Durston, S. (2005). Imaging the developing brain: what have we learned about cognitive development? Trends Cogn Sci, 9(3), 104–110. doi:10.1016/j.tics.2005.01.011 Chocyk, A., Majcher-Maslanka, I., Przyborowska, A., Mackowiak, M. & Wedzony, K. (2015). Early-life stress increases the survival of midbrain 162 Conclusions

/ neurons during postnatal development and enhances reward-related and anxiolytic-like behaviors in a sex-dependent fashion. Int J Dev Neurosci, 44, 33–47. doi:10.1016/j.ijdevneu.2015.05.002 Ducci, F. & Goldman, D. (2012). The genetic basis of addictive disorders. Psychiatr Clin North Am, 35(2), 495–519. doi:10.1016/j.psc.2012.03.010 Giorgio, A., Watkins, K. E., Chadwick, M.,et al. (2010). Longitudinal changes in grey and white matter during adolescence. Neuroimage, 49(1), 94–103. doi:10.1016/j.neuroimage.2009.08.003 Gogtay, N., Giedd, J. N., Lusk, L.,et al. (2004). Dynamic mapping of human cortical development during childhood through early adulthood. Proc Natl Acad Sci U S A, 101(21), 8174–8179. doi:10.1073/pnas.0402680101 Goldman, D., Oroszi, G. & Ducci, F. (2005). The genetics of addictions: uncovering the genes. Nat Rev Genet , 6(7), 521–532. doi:10.1038/ nrg1635 Gottesman, I. I. & Gould, T. D. (2003). The endophenotype concept in psychiatry: etymology and strategic intentions. Am J Psychiatry, 160(4), 636–645. doi:10.1176/appi.ajp.160.4.636 Gottesman, I. I. & Shields, J. (1972).Schizophrenia and Genetics; a Twin Study Vantage Point. New York: Academic Press. Hariri, A. R. (2009). The neurobiology of individual differences in complex behavioral traits. Annu Rev Neurosci, 32, 225–247. doi:10.1146/ annurev.neuro.051508.135335 Hasan, K. M., Sankar, A., Halphen, C.,et al. (2007). Development and organization of the human brain tissue compartments across the lifespan using diffusion tensor imaging.Neuroreport, 18(16), 1735–1739. doi:10.1097/WNR.0b013e3282f0d40c Lebel, C., Caverhill-Godkewitsch, S. & Beaulieu, C. (2010). Age-related variations of white matter tracts.Neuroimage, 52(1), 20–31. doi:10.1016/j.neuroimage.2010.03.072 Meier, M. H., Caspi, A., Ambler, A.,et al. (2012). Persistent cannabis users show neuropsychological decline from childhood to midlife. Proc Natl Acad Sci U S A, 109(40), E2657–E2664. doi:10.1073/pnas.1206820109 Rangaswamy, M. & Porjesz, B. (2008). Uncovering genes for cognitive (dys) function and predisposition for alcoholism spectrum disorders: a review of human brain oscillations as effective endophenotypes. Brain Res, 1235, 153–171. doi:10.1016/j.brainres.2008.06.053 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 References 163

/ Shaw, P., Kabani, N. J., Lerch, J. P., et al. (2008). Neurodevelopmental trajectories of the human cerebral cortex. J Neurosci, 28(14), 3586–3594. doi:10.1523/JNEUROSCI.5309 –07.2008 Villafuerte, S., Heitzeg, M. M., Foley, S.,et al. (2012). Impulsiveness and insula activation during reward anticipation are associated with genetic variants in GABRA2 in a family sample enriched for alcoholism. Mol Psychiatry, 17(5), 511–519. doi:10.1038/mp.2011.33 Wetherill, R. R., Jagannathan, K., Hager, N., Childress, A. R. & Franklin, T. R. (2015). Sex differences in associations between cannabis craving and neural responses to cannabis cues: implications for treatment.Exp Clin Psychopharmacol, 23(4), 238–246. doi:10.1037/pha0000036 Wierenga, L. M., van den Heuvel, M. P., van Dijk, S.,et al. (2016). The development of brain network architecture. Hum Brain Mapp, 37(2), 717–729. doi:10.1002/hbm.23062 164 Conclusions

/ Glossary Accuracy – the ability of an experimental result to conform to an actual, true or correct value or representation. Acetate – a salt that is produced by acetic acid and metabolized by glial cells in the brain. Molecular formula: CH3CO2 À Activation likelihood estimation – an algorithm used to determine coordinate-based activation of specific brain regions from neuroimaging data across multiple studies and subjects. Particularly useful in assessing the convergence of results across many different experiments. Agonist – a molecule or ligand that activates a particular cellular receptor. Allosteric – indirect modulation or regulation via a non-active site. Amotivation – a lack of motivation stemming from detachment or decreased emotion or drive. Anhedonia – a decreased ability to experience pleasure. Antagonist – a molecule or ligand that blocks receptor activation, partially, completely or irreversibly. Appetitiveness – the extent to which a stimuli, object or event elicits an appealing response. Backward masking – a stimulus paradigm in which a stimulus is presented and then almost immediately covered or hidden. This conceptual model is useful for investigating spatiotemporal processing, motion perception, reaction time, etc. Behavior sensitization – an increased motor-stimulant response to a substance that occurs after repeated use and exposure to that substance. β spectral power – the strength of β (frequencies of approximately 13–30 Hz) power contained in the EEG signal. Biomarkers – a wide subcategory of biological or medical signs that can be examined objectively and quantified to indicate normal, pathological or pharmacological effects on biological functioning. They may also indicate disease outcomes, effects of treatment, or environmental exposure to chemicals or nutrients. Cannabinoids – naturally occurring or synthetic compounds that modulate the endocannabinoid system, activating CB1 and CB2 receptors within the body. They may be plant derived (e.g. tetrahydrocannabinol and cannabidiol) or produced by the human body (e.g. anandamide and 2-arachidonoylglycerol). Choline – a molecular precursor to acetylcholine, commonly utilized in magnetic resonance spectroscopy (MRS) to identify the presence of

/ brain tumors. It also serves many other functions throughout the body including neurotransmitter synthesis, cell membrane signaling, liquid transport and methyl group metabolism. Classical conditioning – a mechanism of learning and memory, in which one associates a relevant stimulus with an otherwise, non-relevant stimulus. Typically occurs after repeated exposure to the two stimuli together. Cognitive behavioral model – a theory based on the assumption that mental processes can influence emotional and behavioral (physiological) responses. Cognitive behavioral therapy (CBT) – a type of therapy that seeks to help patients recognize, avoid and cope with the situations in which they are most likely to abuse drugs. Computed tomography (CT) – a type of computerized X-ray imaging that constructs a three-dimensional image from many individual crosssectional X-ray images, taken in succession, of an anatomical region. Used primarily in neuroscience for structural measurements of the nervous system. Contingency management (CM) – a method that uses positive reinforcement such as providing rewards or privileges for remaining drug free, for attending and participating in counseling sessions, or for taking treatment medications as prescribed. Craving – the intense desire to use or obtain a substance. May be continuous, or may occur randomly or after presentation of drugrelated cues. Creatine – an amino acid that is utilized by cells under high-energy demand. This metabolite is commonly targeted in magnetic resonance spectroscopy (MRS) to examine metabolic activity in neurons of the human brain. Cue reactivity – a conditioned response (craving) to various stimuli that are associated (either naturally or through repeated exposure) with drugseeking and drug-taking behaviors. Delay discounting – the tendency to undervalue a reward or punishment that is received after a delayed time period. This concept is thought to be the underlying principle of the tendency of individuals to choose smaller, more immediate rewards over bigger rewards that require a waiting time for receipt. Depressant – a substance that slows the activity of the central nervous system, typically through activation of GABAergic neurons. This category includes sedatives, tranquilizers and alcohol. Diffusivity – the pattern and nature of a substance’s ability to spread (or diffuse) throughout a system. 166 Glossary

/ Dopamine – a neurotransmitter that is prevalent in brain regions that regulate movement, emotion, motivation and reward. Drug expectancy – the cognitive and perceptual outcomes that occur from the anticipated drug effects of the user. Examining this phenomenon can provide insights into drug initiation, reinforcement and sustained use. Drug half-life – the time required for the concentration or amount of drug in the plasma to be reduced by one-half. Dysphoria – the inability to derive pleasure from common non-drug-related rewards. Ecological validity – the extent to which experimental results reflect realworld scenarios or phenomenon. This indicates the relevance of a study to generalize, inform and predict actual, real-world events. Effort–reward calculation – the mental calculation in making a decision of the energetic cost of an action (effort) compared with the benefit of the resulting outcome (reward). Electroencephalography (EEG) – an electrophysiological technique that records electrical conductance of cortical neurons in the brain. This technique is favorable because it is able to obtain this information with high temporal resolution. Emotion regulation – the ability of a person to regulate and modify their emotional experiences and expression. Endophenotype – genetic factors that are determined through genetic testing and are prevalent in association with specific behaviors, illnesses or other psychophysiological factors. The examination of endophenotypes is utilized to better assess gene–environment interactions of psychiatric illnesses. Etiology – the medical pursuit of the cause and origin of a disease. Excitatory post-synaptic potential – the change in electrical conductance of a neuronal membrane at the synapse that increases the likelihood of an action potential. FBJ murine osteosarcoma viral oncogene homolog B (FosB) – an important transcription factor in neural plasticity. This gene is thought to play a vital role in the transition into addiction. It is consider to be the biological mechanism behind the concept of the metaphorical “switch” that is permanently “turned on” in addictive disorders. Fetal alcohol syndrome – a condition that affects the developing embryo and fetus of alcohol-using mothers. It is characterized by distinct facial features and developmental problems. These characteristics include abnormal eye shape, underdeveloped maxillary bones, joint and palmar crease anomalies, cardiac defects, post-natal growth retardation, developmental delay, mental deficiency and central nervous system dysfunction. Glossary 167

/ Final common pathway – the mesolimbic dopamine system, the primary neural circuit responsible for reward processing, which is often referred to as the “final common pathway” as all substances of abuse pharmacologically influence this neurological pathway. It is hypothesized to be the key system effected in reward system dysfunction seen in addiction. Fractional anisotropy – a method for evaluating white matter tracts and calculating the magnitude of directionality of diffusion of these tracts throughout the brain. Glucose metabolism – glucose, the primary energy source for the brain, is processed by the mitochondria inside neurons and other cells in the central nervous system to produce ATP. ATP is then used throughout the cell to carry out many cellular functions. Hallucinogens – typically referred to as psychedelics. These psychoactive substances alter perception, mood and other cognitive functions. Hedonic set point – neurological alterations that occur after repeated substance use and continue down a cyclical path, resulting in a reduced “set point” of reward processing, meaning that everyday rewarding experiences are no longer as pleasurable as they once were, leading to continued substance use in the attempt to get back to the original “set point” of reward and pleasure. Heritability – an estimate of the degree of variation in a phenotypic trait in a population that is due to genetic variation between individuals in that population. Homeostasis – the biological concept that an organism will self-regulate in order to maintain stability within its biological systems. Incentive salience – a theory that distinguishes motivation, or“wanting,” from liking or the memory of a rewarding experience of a substance. It proposes that motivation is a critical component of addiction and is essentially responsible for assigning importance and incentive to obtain a drug. Incentive sensitization – a theory of addiction that posits that drug-induced neurological alterations in the reward system cause increased arousal to the drug and motivation to receive and use the drug. This results in a pathological “wanting” to use and obtain the drug, even though the pleasurable effects of the drug remain unchanged. Inhalants – the volatile substances (gases or vapors) that are found in many common household products (gases, liquids, aerosols and some solids). Inhalation is often known as “sniffing,” “huffing,” “bagging” or “spraying.” Inhibitory post-synaptic potential– change in electrical conductance of a neuronal membrane at the synapse that decreases the likelihood of an action potential. 168 Glossary

/ Interoception – the brain’s ability to construct a sense of self by processing awareness of bodily sensations, behavior and cognition. Intoxication – includes the behavioral, physiological, and cognitive effects or alterations produced after a significant amount of a substance is consumed. Intracranial self-stimulation– an experimental method used in laboratory animals to mimic the reinforcing effects of drug administration and produce dopamine signaling. A stimulating electrode is surgically placed in the animal’s brain, specifically in the median forebrain bundle. The animal is given the option to pull a lever/press a button and receive a small electrical stimulation to that area of the brain. Ionic gradient – a concept of biochemistry in which cellular membranes separate electrically charged ions (Na+ , K + , Ca2+ , ClÀ ) through proteins called active transporters. As ionic receptors open, these ionsflow across the membrane and down the concentration gradient, causing a change in the electrical charge of the cell. This physiological mechanism is a critical component of many major biological functions at the cellular level. Late positive potential (LPP)– a slow (300–700 ms) positive event-related potentialthat is thought to measure attention to emotionally salientstimuli. Magnetic resonance imaging (MRI) – a scanning technique that utilizes magnetic fields and radio waves to generate images of internal structures. Magnetic resonance spectroscopy (MRS) – a complimentary technique to magnetic resonance imaging (MRI). This method measures the attachment of hydrogen protons to various molecules, allowing the measurement of different tissues (to assess the mass and region of brain tumors) and various concentrations of brain metabolites. Motivational enhancement therapy (MET) – a therapy that uses strategies to evoke rapid and internally motivated behavior change to stop drug use and facilitate treatment entry. N-Acetylaspartate (NAA) – this molecule is the most reliable metabolic target in magnetic resonance spectroscopy (MRS) and is extremely concentrated throughout the central nervous system. Narcotics – opium, opium derivatives and their partially synthetic substitutes. Derived from the Greek word for“stupor,” narcotics dull the senses and are commonly prescribed for pain relief. Neonatal abstinence syndrome – occurs in babies after in utero exposure to opioids. It is a drug-withdrawal syndrome that includes symptoms of autonomic instability, spastic movements, irritability, poor sucking reflex, impaired weight gain and, in some cases, seizures. Opponent-process theory – a mechanism of homeostasis. For every emotionally responsive event, the brain produces a counteracting, Glossary 169

/ opposite emotional response, drawing the net emotional reaction closer to neutral. If a positive stimulus or event is removed abruptly, the contracting negative response will continue. Pavlovian conditioning – a learning mechanism through the paired association of two stimuli that leads to a new learned response,first described by Ivan Pavlov; also referred to as classical conditioning. Pharmacodynamics – the biomedical study of the interaction between drug concentration, site of action, behavioral and biological effects, time course of action and intensity of effects. Understanding these components is critical in determining dose effects, toxicity and clinical outcomes. Place preference – an experimental protocol to non-invasively measure perceived drug reward in laboratory animals. It is assumed that the more time the animal spends in an area in which it had previously received drug administration, the greater the reward response to that drug. Positron emission tomography (PET)– a non-invasive technique that enables the measurement of physiological functioning in the brain through the utilization of radioactive tracers that measure cerebral blood flow, metabolism, neurotransmitter binding and levels of radiolabeled drugs. Pre-potent response – the most immediate and automatic response that arises in the face of new or relevant stimuli. In many situations, these foremost and immediate responses are inhibited depending on context, environment or the consideration of other information. Probability discounting – the tendency to assign less value to a gain that is received under probabilistic conditions than the same gain received under certain gain. Probability and value become associated, whereby the perceived value of a gain goes down as the probability of receiving it goes down. Pyramidal cell – a type of neuron that is characterized by distinct apical and basal dendritic trees and a pyramid-shaped nucleus. These cells are abundant throughout the central nervous system, particularly in the cortex, hippocampus and amygdala. Because of their complex structure, they are able to adapt to many diverse and specialized functions. P300 – a positive (P) deflection of voltage and approximately 300 ms latency of stimulus presentation to electrical change in the brain. This neural change in electrical conductance is thought to be elicited by the participant’s cognitive reaction, rather than by a physiological response to a stimulus. Radionucleotides – nucleotides that have been tagged with a radioactive tracer. 170 Glossary

/ Radiotracer – a chemical compound that binds to a particular biological molecule and emits a radioactive signal. This enables the measurement of physiological properties (e.g. receptor binding, diffusion of molecules) of a radiolabeled molecule in living subjects. Reinforcer – any condition that increases the probability of a particular behavior. In the context of addition, it is any cue, situation or object that increases the likelihood of substance use or reinstatement. Reinstatement – a return to substance use after a period of sustained abstinence or extinction of use. Reliability – the consistency of experimental results across measures and/or studies. The importance of reliability is in producing results that are accurate, dependable and reproducible. Resting-state functional connectivity (rsFC)– a type of functional magnetic resonance imaging (fMRI) analysis that examines bloodflow between regions of the brain. This method allows researchers to examine how various cortical regions send signals, communicate and ultimately work with other neural regions during a period of rest. Reward deficiency syndrome – a genetic disorder primarily affecting the DRD2 gene, causing impairment in the functioning of the dopamine D2 receptor and resulting in hypodopaminergic function. These cellular defects lead to impaired reward processing and may predispose individuals to addictive behaviors. Risk factors – characteristics at the biological, psychological, family, community or cultural level that precede and are associated with a higher likelihood of a negative outcome. Single-photon emission computed tomography (SPECT) – a neuroimaging technique that utilizes nuclear medicine and a γ-ray camera to construct a three-dimensional image from multiple two-dimensional images of radioactive distribution throughout the brain. Stimulant – a substance that causes increased arousal and cognitive enhancement through neurochemical effects on monoamines, a class of neurotransmitter that includes norepinephrine and dopamine. Stimulants also stimulate other physiological systems, causing increased heart rate, blood pressure, glucose production and respiration. Superconducting quantum interference device (SQUID) – an extremely sensitive magnetometer, capable of measuring small changes in the magnetic fields of neurons. This method provides high temporal resolution and allows real-time tracking of neuronalfiring sequences. Sympathomimetic – producing physiological effects characteristic of the sympathetic nervous system by promoting the stimulation of sympathetic nerves. Glossary 171

/ Tesla (T) – a measure of the strength or intensity of a magneticfield, typically used to assign magnetic force of a magnetic resonance imaging (MRI) machine: the higher the Tesla value, the higher the resolution of the MRI image. Tolerance – a condition that occurs after repeated substance use, in which more of the substance is required to produce the same level of effect that was experienced at the initial time of use. Tractography – a method of measuring anatomical connections between brain regions that facilitate information transfer and processing across the central nervous system. This imaging tool utilizes magnetic resonance imaging (MRI) technology to map white matter tracts throughout the brain. Transduction – the cellular process of sending or receiving chemical and electrical signals, transferred through the cellular membrane at the synapse, to initiate internal cellular processes inherently and of neighboring cells. Validity – the ability of an assessment or result to accurately measure or represent the intended concept, variable or phenomenon. Validity is dependent on reliability. Withdrawal – a pattern of physical and psychological symptoms that occurs after abrupt cessation of substance use. These symptoms are typically negatively perceived by the user and contribute to the difficulty in remaining abstinent. 172 Glossary

/ Index acamprosate, 134–135 activation likelihood estimation (ALE), 100 acute withdrawal, 85–86 addiction behavioral definition of, 4, 12–14 behavioral progression of, 9–10 and causality, 156–157 chemical, 6–12 as chronic brain disease, 130–132 classification systems of, 6–9 clinical definition/diagnosis of, 2, 6–9 dark side of, 90–91 demography of, 5 mental disorders and, 4 phenomenology of, 4 rates of, 1, 149 stigma of, 5–6 addiction theories allostatic, 36–38 brain disease model, 9–12 cue-elicited craving, 40 future of, 40–41 impaired response inhibition and salience attribution syndrome (iRISA), 38–40 incentive sensitization, 34–36 adolescence, 127, 149–150 Adolescent Brain Cognitive Development (ABCD) study, 32 agonists, 66 Aharonovich, E., 140 Ahmed, S. H., 12 alcohol use action areas of, 68 and anhedonia in protracted withdrawal, 87 appetitiveness, 103 behavioral effects of, 10 brain mechanisms of, 71–73 craving studies, 98–99, 102 demographics of, 5 and dopamine, 53 electrophysiological markers, 89 and endophenotypes, 153–154 intoxication symptoms, 64–65 late positive potential (LPP), 102 pharmacological interventions, 133–135 and social class, 41 stigma of, 5 withdrawal symptoms, 83 allostatic theory, 36–38, 90–91 allosteric potentiator, 134 α power, 88–89 American Psychiatric Association (APA), 6 amotivation, 88 amphetamine use action areas of, 66 behavioral addiction of, 9–10 amygdala volume and alcohol use, 73 and cannabis use, 28 and the cue-elicited craving model, 40 and emotion regulation, 102 Anagnostaras, S. C., 35 anhedonia, 88 antagonists, 66 antireward system, 12 anxiety and cannabis use, 41 and high β activity, 88–89 internet/video game addiction, 94 appetitiveness, 103–105 arterial spin labeling, 100 attention and cognitive behavior therapy (CBT), 135 and craving, 105–106

/ attention deficit/hyperactivity disorder (ADHD), 116–117 Babor, T. F., 5 backward masking, 106 Balleine, B. W., 137 Barratt Impulsiveness Scale, 115, 117 Bauer, L. O., 89 Begleiter, H., 69 behavior prediction, 32 behavior sensitizing experiments, 9 behavioral addiction, 12–14 behavioral drug treatment interventions, 135–137 Berridge, K. C., 35 β power and anxiety, 89–90 and craving, 101 β spectral power, 101 Bickel, W. K., 136 biochemical imaging, 27 –28 biomarkers, 28 blood oxygenated level dependent (BOLD) signal, 25 Bobzean, S. A., 156 Boeijinga, P. H., 135 Boileau, L., 36 Bonson, K. R., 102 brain adolescent, 149–150 drug effects on mesocorticolimbic reward system, 11 brain disease model (addiction), 2, 9–12, 130–132 brain function during protracted withdrawal, 88 during withdrawal, 86 hijacking by drugs, 104–105 and impulsivity, 123 and intoxication, 68–73 and love, 30 measurement of, 22–24 bupropion, 134 cannabis use action areas of, 68 behavioral effects of, 10 craving, 100–101 endocannabinoid system, 53 and endophenotypes, 153 and genetics, 29 longitudinal study of, 156–157 and perceived stress, mood, 40–41 and stress, 161 treatment outcomes, 140 withdrawal symptoms, 83–84 Carroll, K. M., 134, 140 Casey, B. J., 149 Centers for Disease Control and Prevention (CDC), 43 cerebral blood flow (CBF), 86 chemical addiction, 6–12 Childress, A. R., 102–103, 104–105 Chocyk, A., 150 choline, 27 Cicero, T. J., 5 Clark, L., 121, 123 classical conditioning experiments, 9 cocaine action areas of, 68 acute withdrawal, 86 appetitiveness, 103 craving studies, 100 and dopamine, 52 during protracted withdrawal, 88 electrophysiological markers, 88–90 and the iRISA theory, 38 late positive potential (LPP), 102 pharmacological interventions, 134 treatment outcomes, 140 withdrawal symptoms, 83 cognitive behavioral models, 135–137 cognitive behavioral therapy (CBT), 136, 138, 140 cognitive impairment and addiction, 12 compulsive disorders, 12–13 Conklin, C. A., 98 contingency management, 136 Corbit, J. D., 36 Costello, M. R., 137 174 Index

/ craving and the allostatic theory, 38 and attention, 105–106 contextual cues, 102 and the cue-elicited craving model, 40 cue-reactivity paradigms, 99–101 after death, 110 defined and research history, 98–99 neural mechanisms of, 101 neurological underpinnings of, 101–102 neuromolecular mechanisms, 106–107 and reward system hijacking, 103–105 creatine, 27 cue-elicited craving theory, 40 cue-reactivity approach and craving, 99 and methadone, 133 paradigms, 99–101 Dackis, C. A., 86 Dagher, A., 13 Daglish, M. R., 104–105 Decade of the Brain, 130 delay discounting, 115, 123–125, 137 demographics and drug use, 5 and impulsivity, 127 demography of addiction, 5 dendritic alterations (brain), 106–107 depression and cannabis use, 41 genetic risk for, 151 DeWitt, S., 40 diagnosis of addiction, 6–7 Diagnostic and Statistical Manual of Mental Disorders(DSM), 2, 6 diffusivity, 25 diffusion tensor imaging (DTI), 24 disulfiram, 135 Domino, E. F., 69, 88 dopamine and ADHD, 116–117 in behavioral activation and effort, 56 and craving, 100 and hedonistic response, 10–11 and hormones, 156 and reward learning mechanisms, 51–53 and the incentive-sensitization model, 35 and the iRISA theory, 38 during protracted withdrawal, 87–88 dopamine-depletion hypothesis, 86 drug classification, 66 Drug Enforcement Administration (DEA), 2 drug expectations, 75 drug treatment interventions behavioral, 12, 135–137 combined approaches to, 137–139 legislation versus cost, 143–144 outcomes, 138–140 peer influence on, 142–143 pharmacological, 132–135 drug treatment protocol, 131–132 drugs (DEA schedule), 3 Drummond, D. C., 98 Ducci, F., 150, 151 Dunedin Multidisciplinary Health and Development Study, 156–157 Dunning, J. P., 102 dysphoria, 82 ecological validity (craving), 99 –100 ecstasy. See MDMA effort–reward calculation, 56 electroencephalography (EEG) and alcohol endophenotypes, 153–154 and brain mechanism, 69 and craving, 101–102 performance of, 22–24 and withdrawal, 88–90 endophenotype, 118, 150–155 environment, 102 enzyme-linked receptors, 66 Ersche, K. D., 117, 118, 120, 125 etiology of addiction, 6 Evoy, K. E., 134 excitatory post-synaptic potential, 22 FBJ murine osteosarcoma viral oncogene homolog B (FosB), 106–107, 110 Fehr, C., 85 Index 175

/ Feldstein Ewing, S. W., 136 fetal alcohol syndrome, 4 Filbey, F. M., 5, 13, 40 –41, 100, 101, 103–104 final common pathway, 53–54 five-choice serial reaction time task (5CSRTT), 121, 125 food addiction, 12–13 fractional anisotropy, 25 Franken, I. H., 89, 102 Franklin, T. R., 100 functional MRI (fMRI) and adolescence, 60 and backward masking, 105–106 and brain mechanism, 71–73 and cognitive behavioral therapy (CBT), 136 craving studies, 99, 133–134 description of, 25 and sex in addiction, 153 Gallinat, J., 13, 100 gambling addiction, 12 γ-aminobutyric acid (GABA) and acamprosate, 134–135 and acute withdrawal symptoms, 86 and alcohol use, 68, 153 and hedonistic response, 10 gender and addiction, 5, 155–156 gene expression receptors, 66 genetics and addiction, 5, 55–56, 150–155 and drug expectancy, 75 and impulsivity, 118 and limitations to neuroimaging, 29 ΔFosB, 106–107 George, O., 90, 100 Gerbing, D. W., 115 Giorgio, A., 1, 149 Glenn, S. W., 90 glucose metabolism, 70–71 go/no go test, 119 Gogtay, N., 1, 2, 149 Gold, M. S., 85 Goldman, D., 150, 151 Goldstein, R. Z., 38, 39, 139 Gooding, D. C., 89 Gould, K. L., 86 G protein-coupled receptor, 66 Gritz, E. R., 89 half-life (substance), 82–85 Hariri, A. R., 150, 152 Hasan, K. M., 1, 149 hedonistic set point, 35 Heinze, M., 102 Hendriks, V. M., 102 heritability, 150 Herning, R. I., 101 heroin use electrophysiological markers, 88 hijacking the brain, 105 late positive potential (LPP), 102 withdrawal symptoms, 83 Herrmann, M. J., 102 Holden, C., 12 homeostasis, 36–38 Hommer, D. W., 40 hormones and dopamine, 156 hypersensitization, 35 hypothalamic–pituitary–adrenal axis (HPA), 90 impaired response inhibition and salience attribution syndrome (iRISA), 38–40 Impulse Behavior Scale (IBS), 117 impulsivity in adolescence, 127–128 defined, 114–116 and delaying discounting of reward, 123–125 and inhibitory control, 121 nature of, 117–120 neuropharmacology of, 116–117 and risky decision making, 120–121 incentive salience, 35, 47 incentive-sensitization theory, 34–36, 103–104 inhibitory control, 121, 140 inhibitory post-synaptic potential, 22 International Classification of Diseases (ICD), 2, 6 176 Index

/ internet/video game addiction as behavioral addiction, 14 separation anxiety, 94 interoceptive processes, 40 intoxication (drug) action areas of, 66–68 brain mechanisms of, 68–73 defined, 64–65 modulators of, 73–75 pharmacodynamics of, 66 intracranial self-stimulation experiments, 9, 48 ion channel receptors, 66 ionic gradients, 22 Iowa gambling task (IGT), 120–121 Jarvis, M. J., 5 Jessie’s Law, 144 Johnson, T. S., 134 Kalivas, P. W., 53 Ketcherside, A., 41 Kim, J. E., 13 King, D. E., 88 Kish, S., 16 Knott, V. J., 88, 101 Kober, H., 136 Konova, A. B., 137, 139 Koob, G. F., 12, 35, 36, 37, 90–91 Kourosh, A. S., 13 Kuczenski, R., 9 Kuhn, S., 13, 100 Landes, R. D., 136 late positive potential (LPP), 102 Le Moal, M., 35, 36, 37, 90–91 Lebel, C., 1, 149 Leith, N. J., 9 Lenoir, M., 12 Lewis, C. C., 136 ligands, 66 limbic cortex activation, 102 Littel, M., 89 Liu, X., 101 Loughead, J., 133 love and brain function, 30 LSD (lysergic acid diethylamide), 15, 66 magnetic resonance imaging (MRI), 12, 24–27 magnetic resonance spectroscopy (MRS), 27 magnetoencephalography (MEG), 22–23 Marijuana Problem Scale (MPS), 101 Martinotti, G., 87 masked cue task, 105–106 McDonough, B. E., 102 MDMA (3,4- methylenedioxymethamphetamine), 15, 66 mechanisms of addiction, 9–12 memory and addiction, 56–58 mental disorders and addiction, 4 mesolimbic reward system (brain) and behavioral addiction, 13–14 changes during addiction, 10–12 and the cue-elicited craving model, 40 as reward system, 49 metabolites (brain tissue), 27 methadone, 133 methamphetamine use, 53 monetary incentive delay task, 60 morphine, 9 motivation and future drug use prediction, 60 and reward learning mechanisms, 47–58 motivational enhancement therapy (MET), 136 motivational interviewing (MI), 136, 138 Myrick, H., 100, 101 N-acetylaspartate (NAA), 27 Namkoong, K., 102 National Institutes of Health (NIH), 31 natural reinforcers, 12 neonatal abstinence syndrome, 4, 93 Nestler, E. J., 106 neuroimaging studies and addiction activity, 12 and behavior prediction, 32 craving, 99–101 diffusion tensor imaging (DTI), 24 Index 177

/ neuroimaging studies (cont.) electroencephalography (EEG), 22–24, 69, 88–90, 101–102, 153–154 functional MRI (fMRI), 25, 60, 71–73, 99, 105–106, 133–134, 136, 153 and impulsivity, 119 limitations of, 28–29 magnetic resonance imaging (MRI), 24–27 magnetic resonance spectroscopy (MRS), 27 magnetoencephalography (MEG), 22–23 of behavioral addiction, 13–14 of combined drug interventions, 137–138 positron emission tomography (PET), 12, 26–28, 52–53, 69–71, 100 single-photon emission computed tomography (SPECT), 26, 27–28 structural MRI, 24 Niaura, R. S., 98 nicotine use action areas of, 66–68 and brain mechanism, 68–70 and craving, 100, 101–102 and the cholinergic system, 53 delay discounting, 124 demographics of, 5 pharmacological interventions, 133–134 and social class, 41 withdrawal symptoms, 83, 87 nucleus accumbens and acute withdrawal symptoms, 86 and ADHD, 116–117 as common addiction pathway, 54 and craving, 106–107, 109 and dopamine, 49–52 Nutt, D. J., 104 O’Brien, C. P., 13 Ogawa, S., 25 opioid use action areas of, 68 addiction from birth, 93 behavioral effects of, 10 demographics of, 5 and hedonistic response, 11 and the opioid system, 53 pharmacological interventions, 133 as public health concern, 43–45, 162 treatment cost, 143–144 opponent-process theory, 36, 90–91 Orsini, C., 82 P300, 101–102 Pagliaccio, D., 28 Papageorgiou, C. C., 89 Pavlovian conditioning, 98–99 peer recovery specialists, 142–143 pharmacodynamics, 66 pharmacological interventions, 132–135 phencyclidine (PCP), 68 phenomenology of addiction, 4 place preference, 9–10 pleasure molecule. See dopamine Porjesz, B., 69, 89, 153, 154 positron emission tomography (PET) and brain mechanism, 69–71 craving studies, 100 dopamine studies, 27–28, 53 post-acute withdrawal syndrome, 87–88 post-traumatic stress disorder (PTSD), 15–16, 161 Potenza, M. N., 137–138 prefrontal cortex and craving, 100, 106–107 and decision making, 120–121 and dopamine, 51–53 during withdrawal, 86 dysfunction and relapse, 85–86 and the iRISA theory, 38–39 and reinstatement, 54 pre-potent response, 115 probability discounting, 124 Probst, C. C., 12 protracted withdrawal symptoms, 87–88 psychedelic drug therapeutic benefits, 15–16 psychiatric disorders and addiction, 5 pyramidal cells, 22 radionucleotides, 27 radiotracer, 100 178 Index