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

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/ Volkow, N. D., Hitzemann, R., Wolf, A. P.,et al. (1990). Acute effects of ethanol on regional brain glucose metabolism and transport. Psychiatry Res, 35(1), 39–48. doi:10.1016/0925-4927(90)90007-S Volkow, N. D., Wang, G. J., Fowler, J. S., et al. (1999). Blockade of striatal dopamine transporters by intravenous methylphenidate is not sufficient to induce self-reports of “high”. J Pharmacol Exp Ther, 288(1), 14–20. Volkow, N. D., Wang, G. J., Ma, Y., et al. (2005). Activation of orbital and medial prefrontal cortex by methylphenidate in cocaine-addicted subjects but not in controls: relevance to addiction.J Neurosci, 25(15), 3932–3939. doi:10.1523/JNEUROSCI.0433-05.2005 Volkow, N. D., Wang, G. J., Franceschi, D., et al. (2006). Low doses of alcohol substantially decrease glucose metabolism in the human brain. Neuroimage, 29(1), 295–301. doi:10.1016/j.neuroimage.2005.07.004 Volkow, N. D., Kim, S. W., Wang, G. J., et al. (2013). Acute alcohol intoxication decreases glucose metabolism but increases acetate uptake in the human brain. Neuroimage, 64, 277–283. doi:10.1016/j. neuroimage.2012.08.057 Wang, G. J., Volkow, N. D., Franceschi, D., et al. (2000). Regional brain metabolism during alcohol intoxication. Alcohol Clin Exp Res, 24(6), 822–829. World Health Organization (2004). ICD-10, 2nd edn. Geneva: World Health Organization. 80 Intoxication

/ CH A PTE R SIX Withdrawal Learning Objectives • Be able to explain the concept of withdrawal. • Be able to describe the various factors that lead to different manifestations of withdrawal. • Be able to understand the mechanisms that lead to symptoms of withdrawal. • Be able to decipher the different neurobiological mechanisms of acute versus protracted withdrawal symptoms. • Be able to summarize molecular targets that can be used to alleviate withdrawal symptoms. Introduction Withdrawal is a negative state that occurs following cessation from use of a drug that has caused physical dependence. In other words, withdrawal most often occurs in those who have used a drug on a regular basis rather than occasionally. The symptoms of withdrawal often include irritability, insomnia, changes in appetite, restlessness, headaches, nausea and nervousness. Much like other drug effects (i.e. intoxication), withdrawal symptoms vary depending on the type of drug and are influenced by various individual factors, such as frequency and quantity of drug use. Withdrawal symptoms in chronic users of certain drugs such as opiates, alcohol and sedatives can be severe, and sometimes fatal. Withdrawal symptoms also vary throughout the course of abstinence, suggesting different neurobiological mechanisms in acute and protracted abstinence, although both contribute toward the risk for relapse. Why does the brain exhibit these intense symptoms when a drug is no longer in the body? What have we learned about the state of withdrawal that can be used to promote protracted abstinence? Current evidence suggests that withdrawal is the brain’s attempt to adapt to the influx of

/ potent substances. Neural adaptations include the downregulation (or decrease) of receptors (e.g. dopamine in the case of cocaine, opioid receptors in the case of heroin, and γ-aminobutyric acid [GABA] receptors in the case of alcohol). All of these adaptations are in an effort to maintain a balance or homeostasis in the presence of the substance. This chapter will discuss current knowledge on the neurobiological underpinnings of the withdrawal syndrome. The various brain mechanisms underlying the varied withdrawal symptoms will be discussed in addition to the factors that contribute to withdrawal symptoms. What Does Withdrawal Look Like? Just like intoxication symptoms (see Chapter 5), withdrawing from substances can lead to varied presentations depending on the pharmacological mechanisms of the substance (see Table 6.1). However, withdrawal typically manifests in behaviorally opposing ways to the intoxicating effects of a substance. For example, while pupils constrict during opioid intoxication, they dilate during withdrawal. Other somatic disturbances include difficulties with sleep, sweating, tremors, muscle aches and seizures. In general, withdrawal symptoms from all drugs also lead to mood disturbances, although the extent of the disturbances varies depending on the type of drug (see Spotlight 1 for a description of neonatal abstinence syndrome). Negative emotional states (e.g. dysphoria) are characterized by an inability to derive pleasure from common non-drug-related rewards (e.g. food, personal relationships) (see Spotlight 2 for potential negative emotional states following discontinued use of the internet). There are also drug-specific withdrawal effects as outlined in Table 6.1, such as fatigue, decreased mood and psychomotor retardation during psychostimulant withdrawal, whereas amphetamine withdrawal is associated with decreased motivation, such as attenuated responding on a progressive ratio schedule for a sweet solution (Orsini et al., 2001). Withdrawal symptoms also vary by length of abstinence from the drug, and can be classified in terms of whether they are associated with short-term (acute) or long-term (protracted) abstinence from the drug. Acute withdrawal symptoms are those that begin within hours or days after last use of the substance, while protracted withdrawal symptoms are those that go beyond this initial response to the absence of the drug and can persist for months, and sometimes even years. The timeline of withdrawal symptoms is based primarily on each drug’s half-life. The term half-life is a pharmacokinetic parameter 82 Withdrawal

/ Table 6.1 Drug specificity and timing of acute withdrawal symptoms. Drug Onset Duration Characteristics Physical and psychiatric issues Cocaine Depends on administration methods – can begin within hours of last use 3–4 days Sleeplessness or excessive restlessness Increased appetite Depression Paranoia Reduced energy Stroke Cardiovascular collapse Myocardial infarction Organ infarction Violence Severe depression Suicide Alcohol 24–48 h after drop in blood alcohol content 5–7 days Increased blood pressure, heart rate and temperature. Nausea, vomiting and diarrhea Seizures Delirium Death Almost all organ systems are affected: cardiomyopathy, liver disease, esophageal and rectal varices, Korsakoff’s syndrome Fetal alcohol syndrome Heroin Within 24 h of last use 4–7 days Nausea Vomiting Diarrhea Goose bumps Runny nose Teary eyes Yawning Dehydration Neonatal abstinence syndrome Cannabis 3–5 days Up to 28 days Irritability Appetite disturbance Sleep disturbance Nausea Difficulty concentrating Nystagmus Diarrhea What Does Withdrawal Look Like? 83

/ defined by the time it takes for the concentration of the drug in the plasma or the total amount in the body to be reduced by 50%. In other words, after one half-life, the concentration of the drug in the body will be half of the starting dose. For example, as illustrated in Figure 6.1, research suggests that, although the half-life of cannabis is highly variable, it is typically ~3–4 days. Unlike cannabis, other drugs have shorter half-lives, leading to faster onset of withdrawal symptoms following discontinued use, e.g. the half-life of heroin is 12 h, opiates is 8 h, alcohol Table 6.1 (cont.) Drug Onset Duration Characteristics Physical and psychiatric issues Nicotine 1–2 days 1–10 weeks Irritability Anxiety Depression Difficulty concentrating Increased appetite Insomnia Constipation Dizziness Nausea Sore throat Tremors Increased heart rate 0 1 2 3 4 5 6 7 8 9 10 11 Days since last use Severity of symptoms 12 13 Irritability Anger Nervousness Tension Restlessness Lack of appetite Sweating Shakiness Stomach pain Dysphoria Insomnia Craving 14 15 16 17 18 19 20 Figure 6.1 The severity of cannabis withdrawal symptoms across time. 84 Withdrawal

/ is 8 h and benzodiazepines is 24 h. However, as mentioned earlier, the individual experience of withdrawal symptoms varies in severity and duration based on factors such as duration, frequency and quantity of use, metabolism, sex, age, weight, method of intake (e.g. inhaling, injecting, swallowing, snorting), medical and mental health factors, genetic predisposition and the presence of other substances. For example, research suggests that alcohol’s effect on dopamine release is greater for males than for females, which may account for the greater number of men with alcohol use disorder (~10% of the general population) than women (3–5%) (National Institute on Alcohol Abuse and Alcoholism, 2006). Acute Withdrawal Symptoms and Associated Neural Mechanisms The American Society of Addiction Medicine (ASAM) defines acute withdrawal as “the onset of a predictable constellation of signs and symptoms following the abrupt discontinuation of, or rapid decrease in, dosage of a psychoactive substance.” These acute symptoms following discontinuation of drug use have been attributed to uncompensated adaptive changes specific to each drug’s molecular mechanisms and the associated neural adaptations that occur. For example, neuroadaptations in cocaine and stimulant use include dopamine transporter expression increases that result in decreases in the number of post-synaptic dopamine receptors, which then deplete pre-synaptic dopamine (Dackis & Gold, 1985). This dopamine-depleted state following discontinuation of drug use leads to the discomfort associated with withdrawal that drives drug-seeking behavior aimed at restoring dopamine levels. Indeed, empirical studies have found reduced dopamine levels in the nucleus accumbens (an important region in the dopaminergic reward system; see Chapter 4) in those withdrawing from cocaine, morphine, amphetamine and alcohol. Additionally, lower striatal dopamine D2 receptor binding during withdrawal has been found in chronic cocaine (Volkow et al., 1993), alcohol (Volkow et al., 1996), methamphetamine (Volkowet al., 2001) and nicotine users (Fehr et al., 2008). Dopaminergic adaptations likely lead to dysfunction in areas within the dopaminergic reward system, such as prefrontal cortical (PFC) areas (i.e. orbitofrontonal cortex, dorsolateral PFC, anterior cingulate cortex). PFC dysfunction could lead to symptoms that resemble those of major depressive disorder. Indeed, studies in patients with depression show similar deficits in Acute Withdrawal Symptoms and Associated Neural Mechanisms 85

/ PFC function. Dysfunction in PFC areas leads to impaired emotion regulation, which is especially relevant for inhibitory control and coping with stress, and is therefore a strong predictor of relapse (see Sinha & Li, 2007, for a review). In addition to the dopamine-depletion hypothesis, de ficiencies in other neurotransmitter systems also play a role in the homeostatic process during withdrawal. Related to the dopamine-depletion hypothesis, because dopamine signals are transferred through GABA pathways, enhanced sensitivity to the effects (e.g. sleepiness) of GABA-enhancing drugs such as lorazepam has also been observed in the first few days of cocaine withdrawal in chronic cocaine users. This may be due to the downregulation of GABA during chronic cocaine use (Volkow et al., 1998). In addition to dopamine and GABA, other studies have also shown decreases in μ-opioid receptor binding during cocaine withdrawal (Zubieta et al., 1996). In terms of brain function, drug withdrawal is found to be associated with neural responsivity. For example, Volkow et al. (1991) reported that, within 1 week of cocaine withdrawal, cocaine users had higher levels of global brain metabolism (determined by positron emission tomography [PET]) and regional brain metabolism in the basal ganglia and orbitofrontal cortex relative to non-using participants. This increase in metabolism in areas within the dopaminergic reward pathway can therefore also be attributed to dopamine depletion. Reductions in cerebral blood flow (CBF) in the PFC have also been observed in cocaine users during early withdrawal (10 days) relative to healthy controls (Volkow et al., 1988). The authors suggested that this reduction in CBF may be reflective of the effects of vasospasm in cerebral arteries exposed chronically to the sympathomimetic actions of cocaine. In nicotine users, no changes in CBF were noted before and after overnight abstinence; however, subjective withdrawal symptoms were inversely related to CBF in the thalamus (Tanabe et al., 2008). This inverse correlation, as illustrated in Figure 6.2, suggests that the greater the withdrawal symptoms, the less the reduction in thalamic CBF following overnight abstinence. Because it has been shown that individuals with low-grade nicotine withdrawal are more likely to relapse than those with greater withdrawal symptoms that abate quickly, thefindings by Tanabe et al. (2008) of a greater magnitude of CBF change may be the mechanism that underlies the risk for nicotine addiction relapse. Withdrawal from alcohol has also been associated with reductions in glucose metabolism in the striatal-thalamo-orbitofrontal cortex circuit (Volkow et al., 1996). 86 Withdrawal

/ Protracted Withdrawal Symptoms and Associated Neural Mechanisms As opposed to acute withdrawal, protracted withdrawal persists beyond the timeframe of acute withdrawal symptoms and has broader effects. Protracted withdrawal is also referred to as long-term, chronic or postacute withdrawal syndrome and has never been formally accepted by the American Psychological Association (APA). To date, less is known about the mechanisms of protracted withdrawal relative to acute withdrawal. Protracted withdrawal symptoms have been most studied following alcohol abstinence. Anhedonia, which is the decreased ability to experience pleasure, is one of the most common withdrawal symptoms during protracted abstinence and has been observed during withdrawal from alcohol, opioids and other drugs. Martinotti et al. (2008) reported the presence of anhedonia in those abstinent from alcohol for up to 1 year, suggesting the relevance –1 0 1 2 Less withdrawal –15 0 15 Change in thalamic CBF (ml/min/g) 30 More withdrawal 3 Figure 6.2 Change in CBF in the thalamus from baseline to overnight abstinence and subjective withdrawal from nicotine as measured by the Minnesota withdrawal score from baseline to withdrawal. (From Tanabe et al., 2008. © 2007 Springer Nature, USA.) Protracted Withdrawal Symptoms 87

/ of protracted withdrawal in alcohol users. Other symptoms of protracted withdrawal include anxiety, sleep difficulties, short-term memory impairment, fatigue, executive functioning deficits (decision making, inhibitory control) and craving. Symptoms are wide ranging and can include anxiety, hostility, irritability, depression, mood changes, fatigue and insomnia, and have been suggested to last 2 years or longer following cessation of alcohol use. Similar to acute withdrawal, neuroimaging correlates of protracted withdrawal appear also to be hypofunction in dopamine pathways such as decreases in D2 receptor expression and decreases in dopamine release. This reduction in dopamine activity may underlie anhedonia and amotivation during protracted withdrawal. This decreased dopamine activity in the presence of reward persists beyond the presence of acute physical withdrawal from alcohol. Brain function is also reduced during protracted withdrawal in PFC areas such as the dorsolateral prefrontal regions, cingulate gyrus and orbitofrontal cortex, which are important in inhibitory control. Interestingly, the enhanced brain metabolism reported by Volkow et al.(1991) in cocaine patients with less than 1 week’s abstinence described above was not observed in those within 2 –4 weeks after discontinued cocaine use. This suggests a time-dependent attenuation in metabolic activity associated with withdrawal symptoms. Electrophysiological Mechanisms of Withdrawal Electrophysiology studies have advanced our understanding of drug withdrawal and its associated behaviors by quantifying reduced cortical sensitivity through EEG frequency band measures and event-related potential (ERPs). Withdrawal from cocaine has been shown to demonstrate reduced low-frequency waves (i.e. δ and θ), which are correlated with drowsiness (Roemer et al., 1995), but increasedα and β frequencies, which are important for alert states (King et al., 2000). Increased α frequency has also been reported during early withdrawal in heroinaddicted individuals, although this attenuated over time (Shufman et al., 1996). In contrast to the pattern observed during cocaine abstinence, nicotine withdrawal was associated with increased θ frequency, while high-frequency waves such as α and β frequencies were decreased (Domino, 2003). Decreases inα frequency has been associated with a slow reaction time (Surwillo, 1963), diminished arousal and decreased vigilance (Knott & Venables, 1977). These deficits in α activity appear to reverse with protracted abstinence, suggesting that they may be 88 Withdrawal

/ measuring the acute effects of drug withdrawal (Gritz et al., 1975). In terms of ERP measurements during withdrawal, increases in N200 and P300 latencies and decreases in N100 and P300 amplitudes have been reported in those with alcohol use disorder (Porjesz et al., 1987). A reduced P300 amplitude is a consistent finding during cocaine (Gooding et al., 2008), heroin (Papageorgiouet al., 2004) and nicotine (Littel & Franken, 2007) abstinence. These electrophysiological markers could be used to predict relapse, and could therefore play a crucial role in treatment development of addiction. For instance, classification methods based on α and β activity have distinguished with 83–85% accuracy abstinent alcohol users who relapsed from those who remained abstinent (Winterer et al., 1998). In a large prospective study by Bauer (2001), EEG power spectral density during a 3-month abstinence from polysubstance use revealed that an enhanced amount of high-frequency (19.5–39.8 Hz) β activity distinguished patients who would later relapse from those who remained abstinent (Figure 6.3). High β activity reflects hyperarousal and has previously been linked to high anxiety. Furthermore, source localization density analysis localized the fast β activity to deep, anterior regions of the frontal brain, such as the orbitofrontal cortex – an area important for CSD/BEM topographic map of fast β power Relapse-prone group Left hem. Right hem. 0.00597 0.00490 0.00398 0.00299 0.00199 0.000996 0 [uAmm/mm2 ] Current density Abstinence-prone group Figure 6.3 Fast β power can be a predictor of relapse in polysubstance users during a 3-month abstinence. BEM, boundary element method; CSD, current source density. (From Bauer, 2001. © 2001 Springer Nature, USA.) (A black and white version of thisfigure will appear in some formats. For the color version, please refer to the plate section.) Electrophysiological Mechanisms of Withdrawal 89

/ emotion regulation. ERP studies have also distinguished abstainers from relapsers using N200 latency with an overall predictive rate of 71% in alcohol users (Glenn et al., 1993), and P300 amplitude in cocaineaddicted individuals (Bauer, 1997). A Model of Opposing Mechanisms: Between-System Response to Drugs Chapter 3 described models of addiction that were based on opposing processes (i.e. an allostatic model) whereby the initial pleasurable feelings (euphoria, relief from anxiety) from drug use are followed by the opponent process of negative emotional experiences or affective changes such as anxiety, depression and dysphoria. Based on the opponent process theory, withdrawal symptoms are the opposing processes of the acute positively reinforcing actions of drugs. These between-system neuroadaptations (Figure 6.4) occur as a mechanism by which stress modulates both the brain stress and aversive systems to restore normal function despite the presence of drug. Specifically, withdrawal from substances activates both the hypothalamic–pituitary–adrenal (HPA) axis (stress modulation system) and the brain stress/aversive system. The HPA axis is composed of three major structures: the paraventricular nucleus of the hypothalamus, the anterior lobe of the pituitary gland and the adrenal gland. This interaction results in elevated adrenocorticotropic hormone, corticosterone and amygdala corticotropin-releasing factor during acute withdrawal (Koob & Le Moal, 2008). This notion suggests that brain stress systems respond rapidly to changes in homeostasis but are slow to habituate or disengage in this compensatory process (Koob & Le Moal, 2008). It is the prolonged habituation that may lead to the pathological negative states associated with addiction withdrawal (Koob & Le Moal, 2001). This is what has been referred to as the “dark side of addiction.” Evidence to support this comes from studies demonstrating that corticotropin-releasing factor antagonists, delivered intracerebroventricularly or systemically, reverse the anxiogenic-like response during cocaine, nicotine and alcohol withdrawal (George et al., 2007; Koob & Le Moal, 2008). In sum, the negative emotional symptoms during drug withdrawal are associated with between-system changes reflected by a decrease in dopaminergic activity in the mesolimbic dopamine system and with between-system recruitment of neurotransmitter systems that convey stress and anxiety-like effects. Other neurotransmitter systems involved in emotional dysregulation of the motivational effects of drug 90 Withdrawal

/ withdrawal include norepinephrine, substance P, vasopressin, neuropeptide Y, endocannabinoids and nociception (Koob & Le Moal, 2008). Summary Points • Acute withdrawal symptoms begin within hours or days after last use of the substance, while protracted withdrawal symptoms can persist for months, and sometimes even years. External context Stimulus value Action value/cost Anticipation/availability Context Outcome valuation Drug subjective value Action inhibition Emotion control Internal context Incentive to action Stress Affective state Insula HPC CeA Thal GP NAC Craving Action DS ACC Craving Craving dlPFC vlPFC vmPFC OFC – + + + – BNST Insula HPC CeA Thal GP DS Craving dlPFC vlPFC vmPFC OFC + + – BNST Figure 6.4 Neuroadaptations between the reward and stress systems during withdrawal. ACC, anterior cingulate cortex; BNST, bed nucleus of the stria terminalis; CeA, central nucleus of the amygdala; DS, dorsal striatum; dlPFC, dorsolateral PFC; GP, globus pallidus; HPC, hippocampus; NAC, nucleus accumbens; OFC, orbitofrontal cortex; Thal, thalamus; vIPFC, ventrolateral PFC; vmPFC, ventromedial PFC. (Modified from George & Koob, 2013.) Summary Points 91

/ • Decreases in dopamine tone in the nucleus accumbens occur during acute drug withdrawal from all major drugs of abuse. • Neural adaptations that contribute toward withdrawal symptoms include downregulation (or the decrease) of receptors. Review Questions • What are the individual differences that contribute to the highly variable presentation of withdrawal symptoms? • What is the primary determinant of the timeline of drug withdrawal effects? • How does dopamine depletion result in withdrawal symptoms? • How do between-system changes contribute to withdrawal? Further Reading De Biasi, M. & Dani, J. A. (2011). Reward, addiction, withdrawal to nicotine. Annu Rev Neurosci, 34, 105–130. doi:10.1146/annurev-neuro-061010- 113734 Filbey, F. M., Dunlop, J. & Myers, U. S. (2013). Neural effects of positive and negative incentives during marijuana withdrawal.PLoS One, 8(5), e61470. doi:10.1371/journal.pone.0061470 George, O., Koob, G. F. & Vendruscolo, L. F. (2014). Negative reinforcement via motivational withdrawal is the driving force behind the transition to addiction. Psychopharmacology (Berl), 231(19), 3911–3917. doi:10.1007/ s00213-014-3623-1 Myers, K. M. & Carlezon, W. A., Jr. (2010). Extinction of drug- and withdrawal-paired cues in animal models: relevance to the treatment of addiction. Neurosci Biobehav Rev, 35(2), 285–302. doi:10.1016/j. neubiorev.2010.01.011 Negus, S. S. & Banks, M. L. (2018). Modulation of drug choice by extended drug access and withdrawal in rhesus monkeys: implications for negative reinforcement as a driver of addiction and target for medications development. Pharmacol Biochem Behav, 164, 32–39. doi:10.1016/j .pbb.2017.04.006 Piper, M. E. (2015). Withdrawal: expanding a key addiction construct.Nicotine Tob Res, 17(12), 1405–1415. doi:10.1093/ntr/ntv048 92 Withdrawal

/ Spotlight 1 Withdrawn From Birth The opiate epidemic in the USA also impacts unborn infants of opiate-using mothers. Opiate addiction is often initiated through prescription opiates for pain that, left unresolved, can develop into heroin addiction. Heroin, which is cheaper and with longer-lasting effects than prescription opiates, therefore provides an attractive alternative for those with chronic pain, including childbearing women. Weaning from heroin is challenging. In pregnant women, withdrawal symptoms can endanger their pregnancy. However, pregnant women who undergo medication-assisted therapies (i.e. methadone or buprenorphine) endure a condemning stigma. While the circumstancesthat lead to opiate addiction for these women vary, the effects of exposure to drugs in the womb are the same. Most of these infants are born prematurely and suffering from withdrawal, a condition called neonatal abstinence syndrome (NAS). Withdrawal symptoms in babies with NAS are similar to those experienced by adults. These include excessive crying, vomiting, diarrhea, muscle twitches and seizures (Figure S6.1). Fortunately, there is awareness of this problem, and programs have been developed to provide support for these women. Such programs provide women with clinicians to help manage their medication-assisted therapy, and support so that they are able to take care of their families through childcare and education. Such programs have led to reductions in the infants’ length of stay in neonatal intensive care units, e.g. by 33% in Texas (Cleveland et al., 2015). Figure S6.1 Babies have to be weaned from opiates when born from opiate-using mothers. (From https://pixabay.com/en/baby-crying-cry-crying-baby-cute-2387661/.) Spotlight 1 93

/ Spotlight 2 Internet Separation Anxiety With the majority of the population on their electronic devices more hours than not, researchers have begun to ask whether addictive processes may be involved in the use of these devices and their applications (Figure S6.2). A study by Reed et al. (2017) examined the behavioral symptoms of being away from internet use, and found similarities with withdrawal symptoms from drug addiction. They discovered that people who spend an extended amount of time on the internet experience increased heart rate and rises in blood pressure after they stop using the internet. The study was based on 144 adults aged 18–33. The authors warn that these physiological changes may lead to anxiety as well as to hormonal imbalances. Only time will tell what the long-term effects of excessive electronic device use on public health and society will be, but government organizations are already feeling the pressure to create policies. For example, the Ethiopian government recently shut down internet access across the entire country to support students studying for their national examinations. This is in addition to the goal of preventing examination questions being leaked online. References Bauer, L. O. (1997). Frontal P300 decrements, childhood conduct disorder, family history, and the prediction of relapse among abstinent cocaine abusers. Drug Alcohol Depend, 44(1), 1–10. doi:10.1016/S0376-8716 (96)01311-7 Figure S6.2 Can Facebook be addictive? (From https://pixabay.com/en/facebook-social-media-addiction-2387089/.) 94 Withdrawal

/ (2001). Predicting relapse to alcohol and drug abuse via quantitative electroencephalography. Neuropsychopharmacology, 25(3), 332–340. doi:10.1016/S0893-133X(01)00236-6 Cleveland, L., Paradise, K., Borsuk, C., Coutois, B. & Ramirez, L. (2015). The Mommies Toolkit: Improving Outcomes for Families Impacted by Neonatal Abstinence Syndrome. Austin, TX: Texas Department of State Health Services. Available at: www.dshs.texas.gov/sa/NAS/ Mommies_Toolkit.pdf Dackis, C. A. & Gold, M. S. (1985). New concepts in cocaine addiction: the dopamine depletion hypothesis. Neurosci Biobehav Rev, 9(3), 469–477. doi:10.1016/0149-7634(85)90022-3 Domino, E. F. (2003). Effects of tobacco smoking on electroencephalographic, auditory evoked and event related potentials. Brain Cogn, 53(1), 66–74. doi:10.1016/S0278-2626(03) 00204-5 Fehr, C., Yakushev, I., Hohmann, N.,et al. (2008). Association of low striatal dopamine D2 receptor availability with nicotine dependence similar to that seen with other drugs of abuse.Am J Psychiatry, 165(4), 507–514. doi:10.1176/appi.ajp.2007.07020352 George, O. & Koob, G. F. (2013). Control of craving by the prefrontal cortex. Proc Natl Acad Sci U S A, 110(11), 4165–4166. doi:10.1073/ pnas.1301245110 George, O., Ghozland, S., Azar, M. R.,et al. (2007). CRF–CRF1 system activation mediates withdrawal-induced increases in nicotine selfadministration in nicotine-dependent rats.Proc Natl Acad Sci U S A, 104(43), 17198–17203. doi:10.1073/pnas.0707585104 Glenn, S. W., Sinha, R. & Parsons, O. A. (1993). Electrophysiological indices predict resumption of drinking in sober alcoholics.Alcohol, 10(2), 89–95. doi:10.1016/0741-8329(93)90086-4 Gooding, D. C., Burroughs, S. & Boutros, N. N. (2008). Attentional deficits in cocaine-dependent patients: converging behavioral and electrophysiological evidence. Psychiatry Res, 160(2), 145–154. doi:10.1016/j.psychres.2007.11.019 Gritz, E. R., Shiffman, S. M., Jarvik, M. E.,et al. (1975). Physiological and psychological effects of methadone in man. Arch Gen Psychiatry, 32(2), 237–242. doi:10.1001/archpsyc.1975.01760200101010 King, D. E., Herning, R. I., Gorelick, D. A. & Cadet, J. L. (2000). Gender differences in the EEG of abstinent cocaine abusers. Neuropsychobiology, 42(2), 93–98. doi:10.1159/000026678 Knott, V. J. & Venables, P. H. (1977). EEG alpha correlates of non-smokers, smokers, smoking, and smoking deprivation.Psychophysiology, 14(2), 150–156. doi:10.1111/j.1469-8986.1977.tb03367.x References 95

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/ Surwillo, W. W. (1963). The relation of simple response time to brain-wave frequency and the effects of age. Electroencephalogr Clin Neurophysiol, 15, 105–114. doi:10.1016/0013-4694(63)90043-9 Tanabe, J., Crowley, T., Hutchison, K., et al. (2008). Ventral striatal blood flow is altered by acute nicotine but not withdrawal from nicotine. Neuropsychopharmacology, 33(3), 627–633. doi:10.1038/sj. npp.1301428 Volkow, N. D., Mullani, N., Gould, K. L., Adler, S. & Krajewski, K. (1988). Cerebral blood flow in chronic cocaine users: a study with positron emission tomography. Br J Psychiatry, 152(5), 641–648. doi:10.1192/ bjp.152.5.641 Volkow, N. D., Fowler, J. S., Wolf, A. P.,et al. (1991). Changes in brain glucose metabolism in cocaine dependence and withdrawal. Am J Psychiatry, 148(5), 621–626. doi:10.1176/ajp.148.5.621 Volkow, N. D., Fowler, J. S., Wang, G. J.,et al. (1993). Decreased dopamine D2 receptor availability is associated with reduced frontal metabolism in cocaine abusers. Synapse, 14(2), 169–177. doi:10.1002/syn.890140210 Volkow, N. D., Wang, G. J., Fowler, J. S., et al. (1996). Decreases in dopamine receptors but not in dopamine transporters in alcoholics. Alcohol Clin Exp Res, 20(9), 1594–1598. doi:10.1111/j.1530-0277.1996. tb05936.x et al. (1998). Enhanced sensitivity to benzodiazepines in active cocaineabusing subjects: a PET study. Am J Psychiatry, 155(2), 200–206. doi:10.1176/ajp.155.2.200 Volkow, N. D., Chang, L., Wang, G. J., et al. (2001). Low level of brain dopamine D2 receptors in methamphetamine abusers: association with metabolism in the orbitofrontal cortex.Am J Psychiatry, 158(12), 2015–2021. doi:10.1176/appi.ajp.158.12.2015 Winterer, G., Kloppel, B., Heinz, A., et al. (1998). Quantitative EEG (QEEG) predicts relapse in patients with chronic alcoholism and points to a frontally pronounced cerebral disturbance.Psychiatry Res, 78(1–2), 101–113. doi:10.1016/S0165-1781(97)00148-0 Zubieta, J. K., Gorelick, D. A., Stauffer, R.,et al. (1996). Increased mu opioid receptor binding detected by PET in cocaine-dependent men is associated with cocaine craving. Nat Med, 2(11), 1225–1229. doi:10.1007/s00213-008-1225-5. References 97

/ CH A P TER SEVE N Craving Learning Objectives • Be able to understand the problems in the conceptualization of craving. • Be able to describe neuroimaging approaches to examine cue-elicited craving. • Be able to explain what is meant by the statement that drugs“hijack” the brain. • Be able to discuss studies demonstrating how craving and attention are separate processes. • Be able to summarize the role of ΔFosB in craving. Introduction Craving is often defined as a strong subjective desire to use alcohol or drugs. Historically, there has been debate with regard to the conceptualization and measurement of craving (see reviews by Tiffany & Conklin, 2000; Tiffany et al., 2000). Craving can be measured in terms of physical manifestations or psychological experiences. Craving can therefore be viewed as a multidimensional construct involving subjective, behavioral or physiological responses. Research stemming from the 1980s advanced the study of craving by incorporating a cue-reactivity approach. During cue reactivity, individuals are exposed to drug cues (e.g. the sight of drug paraphernalia or the smell of alcohol), which are linked to self-report measures of craving. The measurement of craving in this context of cue reactivity is grounded on theoretical learning theory frameworks, such as Pavlovian conditioning. Cue-reactivity research has also emphasized experimental control in an attempt to improve reliability and validity in the measurement of craving (Drummond, 2000; Niauraet al., 1988). The notion of craving has historically been criticized for its subjective nature, which does not prospectively predict drug-use behavior (Tiffany et al., 2000). There were

/ also concerns with regard to the ecological validity of the use of subjective measures in laboratory settings that question their accuracy, reliability and validity. Translating cue-elicited craving to classical approaches from animal models has also been a challenge. For example, subjective craving is not easily discerned in animals; thus, direct translation of the multidimensional construct of craving may not be possible from animal models to humans. As discussed in Chapter 4, findings from the animal literature have shown that the motivation to use drugs is linked to the actions of drugs on the mesocorticolimbic pathways in the brain, which are the neural substrates that putatively underlie the attribution of incentive salience to alcohol and other drugs of abuse (Berridge & Robinson, 1998; Robinson & Berridge, 1993; Wise, 1988). Recently, scientists have begun to use neuroimaging approaches to focus on the neurobiology of craving in humans. The use of more objective neuroimaging techniques alleviates the burden of proof on subjective responses, thus addressing some of the limitations of accuracy and validity in behavioral investigations. Neuroimaging approaches also allow greater consistency between animal and human models because of the focus on neurobiology. This chapter will focus on the various techniques that demonstrate the presence of cue-elicited craving across different substances and that led to the addition of craving as a primary symptom for the diagnosis of a substance use disorder (SUD) in the DSM-5. Cue-Elicited Craving Paradigms and Associated Neural Mechanisms Cue-elicited craving paradigms entail exposing individuals to substancerelated cues and linking the event to a subjective measure of craving. Cue-elicited craving paradigms consist of a variety of sensory modalities including visual, olfactory, auditory and tactile presentations. One of the earliest studies used ethanol odor to elicit subjective craving in alcohol users (Schneider et al., 2001). The functional magnetic resonance imaging (fMRI) results showed that increases in the neural response in the cerebellum and amygdala during the smell of ethanol were positively correlated with subjective craving for alcohol. Visual cue modalities are the most widely used. These paradigms involve visual presentations of cue images, such as drug paraphernalia. For example, a study by Wrase et al. (2002) presenting visual alcohol stimuli to participants showed significant activation in the fusiform gyrus, basal ganglia and orbitofrontal gyrus compared with abstract control Cue-Elicited Craving Paradigms 99

/ pictures. Videos have also been utilized to study craving (Wrase et al., 2002). For example, using positron emission tomography (PET), current cocaine users were exposed to a 10 min videotape of persons using cocaine as well as a 45 min audiotape of pleasurable experiences from cocaine use (taken from actual interviews with cocaine abusers) (Wong et al., 2006). The results of this study found that displacement of the radiotracer [ 11C]raclopride, which is a measure of occupancy at D2 -like receptors, increased in the putamen of participants who reported cueelicited craving compared with those who did not. Furthermore, the intensity of the self-reported craving was positively correlated with the increase in dopamine receptor occupancy, suggesting increased release of intrasynaptic dopamine in the putamen. These results provide support for the role of dopamine in the dorsal striatum during the subjective experience of craving. To address the issue of ecological validity, some cue-elicited craving paradigms have also used a combination of modalities to mimic realworld scenarios. For instance, simultaneous presentation of taste (sip of alcohol) and visual (picture of alcohol stimuli) cues revealed that alcohol cues increase activation in the prefrontal cortex (PFC) (George et al., 2001) and limbic areas (Myrick et al., 2004). A study by Franklin et al. (2007) presented tactile cues (cigarettes) in conjunction with smoking cue-related videos during arterial spin labeling. They found greater activation compared with a neutral cue in the amygdala, ventral striatum, hippocampus, insula, orbitofrontal cortex and thalamus (Franklin et al., 2007). Studies by Filbey et al. (2016) using fMRI used simultaneous presentation of tactile and visual cannabis cues (cannabis paraphernalia) (Figure 7.1). This study also found positive brain behavior correlations between the neural response to cannabis cues in frontostriatal–temporal regions and subjective craving, cannabis-related problems, withdrawal symptoms and levels of tetrahydrocannabinol (THC) metabolites (cluster-threshold z = 2.3, P<0.05). A quantitative meta-analysis offindings from cue-reactivity neuroimaging studies was conducted by Kuhn and Gallinat (2011). They performed activation likelihood estimation to determine overlaps in brain mechanisms elicited by cue-induced craving paradigms in nicotine, alcohol and cocaine users. Their results found a consistent ventral striatum response, and to a lesser degree, anterior cingulate and amygdala responses, to drug cues. These regions may therefore reflect the core circuit of drug craving. Importantly, these brain responses are correlated with the subjective experience of craving. Additionally, they are also correlated with addiction severity, such that the greater the response in these areas to cues, the 100 Craving

/ greater the severity of symptoms related to addiction. For example, in alcohol users, Myrick et al. (2004) reported that alcohol-dependent individuals demonstrated a greater blood oxygenated level-dependent (BOLD) response in the PFC and anterior limbic areas after a sip of alcohol and exposure to visual alcohol cues relative to non-dependent alcohol users. Similarly, in cannabis users, the pattern of activation was significantly positively correlated with drug-related problems as measured by the Marijuana Problem Scale (MPS) (Filbey et al., 2009). Neurophysiological Underpinnings of Craving EEG has also been used to investigate cue-elicited craving in addiction. In cocaine users, EEG studies have found high β spectral power in response to cocaine-related cues (Liu et al., 1998; Reid et al., 2003). These β states are the states associated with normal waking consciousness. These increases in β power have also been associated with greater subjective craving (Herninget al., 1997). Similar increases in β spectral power have been reported in nicotine users in response to cigarette-related cues (Knott et al., 2008a, 2008b). Event-related potential (ERP) studies also report higher cortical activation in OR Please rate your urge to use marijuana right now No urge 0 1 2 3 4 5 6 7 8 9 10 Extremely at all high urge + Cue exposure 20 s 20 s 5 s Rate Washout Figure 7.1 Cue-elicited craving paradigm using tactile cannabis cue paraphernalia, a neutral object (pencil) and appetitive non-drug reward cues (fruit, not shown). (From Filbey et al., 2016.) Neurophysiological Underpinnings of Craving 101

/ response to drug cues such as increased P300 amplitude has been reported in response to drug cues in alcohol (Herrmann et al., 2000) and nicotine (Warren & McDonough, 1999) users. P300 is a positive deflection in voltage that occurs between 250 and 500 ms following the onset of a stimulus and has been associated with engagement of attention (such as orienting) to stimuli. Increased late positive potential (LPP) amplitudes have also been reported in response to drugrelated pictures compared with neutral pictures in individuals addicted to alcohol (Heinze et al., 2007; Herrmannet al., 2001; Namkoong et al., 2004), cocaine (Dunning et al., 2011; Franken et al., 2003; van de Laar et al., 2004) and heroin (Franken et al., 2003). LPPs have a latency (delay between stimulus and response) of 400–500 ms after the onset of a stimulus and have been suggested to facilitate attention to emotional stimuli. Taken together, EEG studies of cue-elicited craving in addiction suggest that greater cortical arousal – in the form of increased β, P300 and LPP amplitudes during drug cues – is linked to greater subjective craving. Contextual Cues In addition to drug cues as described above, environmental or contextual cues that have been associated with drug use can also trigger drug craving. The brain mechanisms that underlie the response to contextual cues appear to involve a more distributed neural network from that underlying craving in response to drug cues. This network includes brain regions that subserve emotional and cognitive aspects of memory in the link between environmental cues and craving. Paradigms that use contextual cues utilize individual evocative scripts that ask participants to imagine themselves in a setting where they would have been using cocaine. In addition, neutral scripts, such as those that ask participants to imagine themselves making art, are also presented. The scripts included vivid descriptions of emotions and sensations of the activities. In one such study, Bonson et al. (2002) reported that the presentation of “evocative scripts” that described the context where drug use occurs in the individuals, in combination with videos and paraphernalia related to cocaine, elicited activation of the lateral amygdala, an important area for emotion regulation. These findings replicated earlier reports of the involvement of areas in the limbic system important for processing emotion and memory in response to cocaine cues (Childress et al., 1999). Taken together, cue-elicited studies of cocaine show that limbic cortex activation is a component of cue-induced craving. 102 Craving

/ Do Drugs Hijack the Reward Circuitry of the Brain? As described above, the literature suggests that subjective craving is correlated to the brain’s response in the reward circuitry (described in Chapter 4). The question then becomes whether these increased brain responses to drug cues are due to general hypersensitivity to salient stimuli, as would be suggested by the reward deficiency syndrome, or whether this hyper-responsivity is specific to drug and alcohol cues. Early cue-elicited craving paradigms compared drug cues with neutral cues. For instance, early studies in alcohol craving compared alcohol taste relative to neutral tastes such as water or artificial saliva. Thus, whether a differential response in the brain to alcohol tastes relative to neutral tastes were driven by alcohol-specific craving processes or by the general appetitiveness of the alcohol taste relative to water or artificial saliva remained unknown. Subsequent studies, such as those by Filbey et al. (2008), integrated control cues of equal appetitiveness to address this concern. For example, one such study delivered small amounts of alcohol to heavydrinking adults and compared the brain’s response relative to a sweet yet unfamiliar taste, such as litchi juice (Filbey et al., 2008). The results showed that the taste of an alcoholic beverage is a very powerful cue, producing a significant BOLD response in the striatum, ventral tegmental area (VTA) and PFC, above and beyond that of an appetitive and novel cue. Other studies have also reported similar findings of drugrelated activation in similar pathways for natural rewards. For example, Childress et al. (2008) compared cocaine cues with sexual cues (in addition to neutral and aversive cues) in male cocaine patients, and found increased activity encompassing the ventral pallidum/amygdala in response to cocaine cues relative to sexual cues (Figure 7.2). These findings suggest that cocaine leads to greater activation in a primordial brain circuitry that encodes evocative stimuli. A similar approach was also applied in cannabis use where tactile and visual cues for cannabis cues were compared with neutral cues as well as appetitive non-drugreward cues (Figure 7.1) (Filbey et al., 2016). For the appetitive cues, participants were presented with their preferred fruit. The authors found that exposure to cannabis cues in long-term daily cannabis users elicited a greater response in the orbitofrontal cortex, striatum, anterior cingulate gyrus and VTA relative to that in non-users. These findings demonstrate hyper-responsivity and specificity of the brain response to cannabis cues in long-term cannabis users that are above the response to natural reward cues. These observations are concordant with Do Drugs Hijack the Reward Circuitry of the Brain? 103

/ incentive-sensitization models, suggesting sensitization of the mesocorticolimbic regions and disruption of the natural reward processes following drug use. According to Daglish and colleagues, the brain networks involved in drug craving are the same networks as for various cognitive processes such as emotion, attention and memory, in addition to reward processing (Daglish & Nutt, 2003; Daglish et al., 2003). However, in the case of addiction, these networks become hypersensitive to drug-related cues. In other words, the brain is “hijacked” by drugs, which is in line with the incentive-sensitization model (see Chapter 3). This idea stems from findings that illustrate that the difference in users and non-users is not the involvement of these various cognitive networks but the degree to which they are engaged in the users (e.g. heroin users in the study by Sell et al., 2000). As mentioned in the previous section, studies illustrate that subjective craving is correlated strongly with activation increases in the reward pathway (orbitofrontal cortex and striatum), as well as areas Null “Unseen” cue paradigm + Sexual Neutral Aversive Cocaine Figure 7.2 Cue-elicited craving paradigm. A study by Childresset al. (2008) found a greater response to cocaine than to sexual (also aversive and neutral) cues. 104 Craving

/ related to memory (hippocampus, PFC), emotion (amygdala) and attention (anterior cingulate gyrus, PFC). Functional connectivity between these regions has been shown to reflect the ability of drug cues to activate attentional and memory circuits to a greater degree than nondrug cues. Greater Craving or Greater Attention? The idea presented by Daglishet al.(2003) that the ability of drug cues to activate attentional and memory circuits to a greater degree than nondrug cues underlines craving suggests that craving may simply be attention. Studies by Childress et al. (2008) and Young et al. (2014) that used masked cues have shed some light on this topic and support the notion that craving is implicit, i.e. occurs subconsciously and only occasionally intrudes into conscious awareness (Tiffany & Wray, 2012). These studies utilized backward-masked images of cocaine, sexual, aversive and neutral cues and presented them rapidly (i.e. 33 ms) (Figure 7.3). Backward masking presents a masked stimulus immediately after another brief Fixation cross Target stimulus Masking stimulus Fixation cross 2000 ms 467 ms 1000 ms 33 ms 500 ms + + Figure 7.3 Representative trial from the backward-masked cue task. In each trial, participants were presented with the following visual stimuli in immediate succession: crosshair (500 ms); target stimulus (33 ms); masking stimulus (467 ms); crosshair (1000 ms). Target images were presented from one of four categories: cocaine (shown), neutral, sexual and aversive. (From Young et al., 2014. © 2014 Society for Neuroscience, USA.) Greater Craving or Greater Attention? 105

/ target stimulus, which often leads to a failure to perceive the masked stimulus, in order to examine pre-attentive processes. These studies found evidence for involvement of the limbic cortex during the masked or subconscious exposure to the drug and sexual cues that correlated with positive affect to the visible versions of the same cues. Neuromolecular Mechanisms The idea that craving occurs after the drug is consumed suggests the occurrence of neural adaptations following drug exposure. One of the cellular changes triggered by drug use is increased dendritic structure via increased dendritic spine density in the nucleus accumbens and PFC. Nestler and colleagues suggested that these dendritic alterations are mediated by transformation of FBJ murine osteosarcoma viral oncogene homolog B (FosB) to ΔFosB (Figure 7.4) (Nestler, 2001; Nestleret al., 2001). FosB is a transcription factor in the brain, which, together with other molecules, is involved in signal transduction that conveys genetic information between the cells and also determines activation of certain genes. This transformation is initiated by increases in dopamine following drug exposure, which increases with continued drug exposure (i.e. chronic use). In terms of transduction, ΔFosB inactivates the Normal responses to drugs Use-dependent plasticity leading to sensitized responses to drug and environmental cues Repeated drug exposure (e.g. via neurotrophic factors, FosB, CDK5?) Figure 7.4 Regulation of the dendritic structure by drugs of abuse. Expansion of a neuron’s dendritic tree and spine density occurs after chronic exposure to a drug of abuse in the nucleus accumbens and PFC, mediated byΔFosB and the consequent induction of CDK5. (From Nestler et al., 2001. © 2001 Springer Nature, USA.) 106 Craving

/ dynorphin gene (encoding dynorphins, which are endogenous opioids) and activates the cyclin-dependent kinase 5 gene (CDK5) that encodes cell division protein CDK5, a protein involved in neuronal maturation and migration. The CDK5 protein stimulates dendritic spine growth in the nucleus accumbens, which leads to craving and drug sensitivity. ΔFosB influences growth factors and structural changes (neuronal plasticity) in the brain – approximately in the region where memory is formed. The fact that these mechanisms resemble those in some learning models (e.g. long-term potentiation) suggests that ΔFosB may mediate cueelicited craving.ΔFosB is stable, therefore initiating and sustaining these changes in gene expression long after drug use ceases. Transgenic mice studies have shown that animals with overexpression of ΔFosB have increased sensitivity to the effects of drugs. Consequently, ΔFosB has been posited as a “molecular switch” that converts the acute response to drugs into long-term responses, such as craving. See Spotlight for a description of how post-mortem ΔFosB may indicate the persistence of physiological craving. Summary Points • The conceptualization of craving has been advanced by neuroimaging techniques. • Neuroimaging studies demonstrate a heightened brain response in wide brain networks encompassing reward, attention, emotion and memory systems. • Patterns of brain response to drug cues are greater than those to natural rewards and are correlated with subjective craving as well as with indices of addiction severity. • EEG studies show heightened arousal in response to drug cues. • Backward masking provides evidence for the subconscious awareness of drug cues. • ΔFosB mediates the neural changes, including craving, that occur following drug exposure. Review Questions • What were the criticisms in the conceptualization of craving? • What are the wider systems that integrate to underlie craving in response to drug cues? Review Questions 107

/ • What is the primary finding of EEG studies during cue-elicited craving? • Describe the process of backward masking and what this has approach answered in terms of drug craving? • How can ΔFosB be a marker of addiction? Further Reading Ekhtiari, H., Nasseri, P., Yavari, F., Mokri, A. & Monterosso, J. (2016). Neuroscience of drug craving for addiction medicine: from circuits to therapies. Prog Brain Res, 223, 115–141. doi:10.1016/bs.pbr.2015.10.002 Filbey, F. M. & DeWitt, S. J. (2012). Cannabis cue-elicited craving and the reward neurocircuitry. Prog Neuropsychopharmacol Biol Psychiatry, 38(1), 30–35. doi:10.1016/j.pnpbp.2011.11.001 Filbey, F. M., Schacht, J. P., Myers, U. S., Chavez, R. S. & Hutchison, K. E. (2009). Marijuana craving in the brain.Proc Natl Acad Sci U S A, 106(31), 13016–13021. doi:10.1073/pnas.0903863106 Grant, S., London, E. D., Newlin, D. B.,et al. (1996). Activation of memory circuits during cue-elicited cocaine craving. Proc Natl Acad Sci U S A, 93(21), 12040–12045. Gu, X. & Filbey, F. (2017). A Bayesian observer model of drug craving.JAMA Psychiatry, 74(4), 419–420. doi:10.1001/jamapsychiatry.2016.3823 Myrick, H., Anton, R. F., Li, X., et al. (2004). Differential brain activity in alcoholics and social drinkers to alcohol cues: relationship to craving. Neuropsychopharmacology, 29(2), 393–402. doi:10.1038/sj.npp.1300295 Robinson, T. E. & Berridge, K. C. (1993). The neural basis of drug craving: an incentive-sensitization theory of addiction. Brain Res Brain Res Rev, 18(3), 247–291. Tiffany, S. T., Carter, B. L. & Singleton, E. G. (2000). Challenges in the manipulation, assessment and interpretation of craving relevant variables. Addiction, 95, Suppl. 2, S177–S187. Tiffany, S. T. & Wray, J. M. (2012). The clinical significance of drug craving. Ann N Y Acad Sci, 1248, 1–17. doi:10.1111/j.1749-6632.2011.06298.x Spotlight Drug Cravings Persist in Death The presence of mutated ΔFosB protein weeks after the drug-use event suggests that craving persists for weeks, even after cessation of use. 108 Craving

/ A group of scientists led by Monika Seltenhammer from MedUni in Vienna, Austria, made headlines in 2016 when they published their researchfindings on evidence that drug craving persists in the dead (Seltenhammeret al., 2016). In their study, they examined tissue samples from the nucleus accumbens of fifteen deceased heroin addicts and fifteen non-drug users. They measured levels of ΔFosB and found that accumulation of the protein was still detectable 9 days after death. The scientists referred to this effect as “dependence memory.” From this finding, the scientists inferred that ΔFosB persists even longer in living individuals, perhaps as long as months. This supports existing animal findings of protein differences in live substanceexposed animals relative to non-exposed animals, although lasting far longer in post-mortem human brain tissue. The importance of this discovery is in providing evidence of physiological craving that could be used as a marker of addiction severity, independent of (a) DGsp DGip (d) (f) (g) (e) (b) (c) Figure S7.1 Measuring ΔFosB. Image thresholding analysis of raw FosB/ΔFosB immunoreactivity (a) involves selecting regions of interest (b), then thresholding (b) and magnification (d–g), DGip, infrapyramidal blade of the dentate gyrus; DGsp, suprapyramidal blade of the dentate gyrus. (From Nishijimaet al., 2013.) (A black and white version of thisfigure will appear in some formats. For the color version, please refer to the plate section.) Spotlight 109

/ toxicology. Furthermore, this research underlines the importance of postmortem studies in informing potential mechanisms and targets for treatment for addiction (Figure S7.1). The scientists suggest that activation ofΔFosB can be prevented, and future studies are needed to determine how targeting ΔFosB can be used to treat the onset of addictive behavior. References Bonson, K. R., Grant, S. J., Contoreggi, C. S.,et al. (2002). Neural systems and cue-induced cocaine craving. Neuropsychopharmacology, 26(3), 376–386. doi:10.1016/S0893-133X(01)00371-2 Berridge, K. C. & Robinson, T. E. (1998). What is the role of dopamine in reward: hedonic impact, reward learning, or incentive salience? Brain Res Brain Res Rev, 28(3), 309–369. Childress, A. R., Mozley, P. D., McElgin, W.,et al. (1999). Limbic activation during cue-induced cocaine craving. Am J Psychiatry, 156(1), 11–18. doi:10.1176/ajp.156.1.11 Childress, A. R., Ehrman, R. N., Wang, Z.,et al. (2008). Prelude to passion: limbic activation by “unseen” drug and sexual cues. PLoS One, 3(1), e1506. doi:10.1371/journal.pone.0001506 Daglish, M. R. & Nutt, D. J. (2003). Brain imaging studies in human addicts. Eur Neuropsychopharmacol, 13(6), 453–458. doi:10.1016/j. euroneuro.2003.08.006 Daglish, M. R., Weinstein, A., Malizia, A. L.,et al. (2003). Functional connectivity analysis of the neural circuits of opiate craving: “more” rather than “different”? Neuroimage, 20(4), 1964–1970. doi:10.1016/j. neuroimage.2003.07.025 Drummond, D. C. (2000). What does cue-reactivity have to offer clinical research? Addiction, 95 Suppl 2, S129–144. doi:10.1080/ 09652140050111708 Dunning, J. P., Parvaz, M. A., Hajcak, G.,et al. (2011). Motivated attention to cocaine and emotional cues in abstinent and current cocaine users – an ERP study. Eur J Neurosci, 33(9), 1716–1723. doi:10.1111/j.1460- 9568.2011.07663.x Filbey, F. M., Claus, E., Audette, A. R.,et al. (2008). Exposure to the taste of alcohol elicits activation of the mesocorticolimbic neurocircuitry. Neuropsychopharmacology, 33(6), 1391–1401. doi:10.1038/sj. npp.1301513 Filbey, F. M., Schacht, J. P., Myers, U. S., Chavez, R. S. & Hutchison, K. E. (2009). Marijuana craving in the brain. Proc Natl Acad Sci U S A, 106(31), 13016–13021. doi:10.1073/pnas.0903863106 110 Craving

/ Filbey, F. M., Dunlop, J., Ketcherside, A.,et al. (2016). fMRI study of neural sensitization to hedonic stimuli in long-term, daily cannabis users.Hum Brain Mapp, 37(10), 3431–3443. doi:10.1002/hbm.23250 Franken, I. H., Stam, C. J., Hendriks, V. M. & van den Brink, W. (2003). Neurophysiological evidence for abnormal cognitive processing of drug cues in heroin dependence. Psychopharmacology (Berl), 170(2), 205–212. doi:10.1007/s00213-003-1542-7 Franklin, T. R., Wang, Z., Wang, J., et al. (2007). Limbic activation to cigarette smoking cues independent of nicotine withdrawal: a perfusion fMRI study.Neuropsychopharmacology, 32(11), 2301–2309. doi:10.1038/sj.npp.1301371 George, M. S., Anton, R. F., Bloomer, C.,et al. (2001). Activation of prefrontal cortex and anterior thalamus in alcoholic subjects on exposure to alcohol-specific cues. Arch Gen Psychiatry, 58(4), 345–352. doi:10.1001/archpsyc.58.4.345 Heinze, M., Wolfling, K. & Grusser, S. M. (2007). Cue-induced auditory evoked potentials in alcoholism. Clin Neurophysiol, 118(4), 856–862. doi:10.1016/j.clinph.2006.12.003 Herning, R. I., Guo, X., Better, W. E.,et al. (1997). Neurophysiological signs of cocaine dependence: increased electroencephalogram beta during withdrawal. Biol Psychiatry, 41(11), 1087–1094. doi:10.1016/S0006- 3223(96)00258-2 Herrmann, M. J., Weijers, H. G., Wiesbeck, G. A.,et al. (2000). Eventrelated potentials and cue-reactivity in alcoholism. Alcohol Clin Exp Res, 24(11), 1724–1729. doi:10.1016/j.clinph.2006.12.003 Herrmann, M. J., Weijers, H. G., Wiesbeck, G. A., Boning, J. & Fallgatter, A. J. (2001). Alcohol cue-reactivity in heavy and light social drinkers as revealed by event-related potentials. Alcohol Alcohol, 36(6), 588–593. doi:10.1093/alcalc/36.6.588 Knott, V., Cosgrove, M., Villeneuve, C., et al. (2008a). EEG correlates of imagery-induced cigarette craving in male and female smokers. Addict Behav, 33(4), 616–621. doi:10.1016/j.addbeh.2007.11.006 Knott, V. J., Naccache, L., Cyr, E., et al. (2008b). Craving-induced EEG reactivity in smokers: effects of mood induction, nicotine dependence and gender. Neuropsychobiology, 58(3–4), 187–199. doi:10.1159/ 000201716 Kuhn, S. & Gallinat, J. (2011). Common biology of craving across legal and illegal drugs – a quantitative meta-analysis of cue-reactivity brain response. Eur J Neurosci, 33(7), 1318–1326. doi:10.1111/j.1460- 9568.2010.07590.x Liu, X., Vaupel, D. B., Grant, S. & London, E. D. (1998). Effect of cocainerelated environmental stimuli on the spontaneous References 111

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/ CH A P TER EI G H T Impulsivity Learning Objectives • Be able to explain the challenges in defining impulsivity as a unitary construct. • Be able to describe the literature on whether impulsivity is a cause or a consequence of addiction. • Be able to discuss the concepts of risky decision making. • Be able to understand inhibitory control and delay discounting. • Be able to outline the networks and neurotransmitter mechanisms related to impulsivity. Introduction Impulsivity is a multifaceted construct that encompasses a number of concepts bound together by an inability to control one ’s behavior. These concepts include, but are not limited to, risk taking, disinhibition and delay discounting (Figure 8.1). Whether aspects of impulsivity are the cause or effect of substance use remains to be determined. Advances in research show that there may be underlying risks for addiction related to the tendency to be impulsive that may then be further exacerbated by substance use. Innovative research techniques aimed at disentangling the various aspects of impulsivity have noted that different types of impulsivity are associated with different types of substance use. Broadly speaking, impulsivity is the propensity to respond without foresight. Because impulsive behavior can occur as a result of deficits at any stage of response production – response selection, response preparation, response initiation or response execution – defining impulsivity as a unitary concept has been a challenge in empirical research. Dissociable cognitive processes (behavioral and neurobiological) underlie impulsive behavior and differentially contribute toward producing a response. In general, the behavioral tasks used to measure impulsivity

/ determine: 1) the perseverance of a response despite negative consequences; 2) the preference for a small immediate reward over a larger delayed reward; and 3) the ability to withhold a pre-potent response. Although extensive research has focused on understanding the ontology of impulsivity, there continuous to be debate in this field. Work by Gerbing et al. (1987) using a factor analysis on eleven self-report measures and four behavioral tasks revealed three impulsivity factors, which they referred to as spontaneity, persistence and care free. Principal components analysis of a widely used self-report questionnaire, the Barratt Impulsiveness Scale (BSS-11), revealed a three-factor model of impulsivity that includes greater motor activation, less attention and less planning. Overall, these models include the following elements: 1) a decreased sensitivity to negative consequences (risk-taking); 2) rapid, unplanned reactions to stimuli before complete processing of information (impaired inhibitory control); and 3) a lack of regard for long-term consequences (delay discounting). Overall, it is evident that impulsivity, measured in a number of ways, is associated with some forms of drug abuse and seems likely to result from multiple dysfunctions in corticostriatal pathways associated with diverse forms of impulsivity (Figure 8.2). This chapter will review the vast literature that aims to understand the bond between impulsivity and addiction. Emphasis will be given toward Figure 8.1 Impulsivity leads to risky behavior. Introduction 115

/ clarifying the different concepts that underlie the broad construct of impulsivity and the distinct methods used to study each one. Neuropharmacology of Impulsivity Research in attention deficit/hyperactivity disorder (ADHD) has provided insight into the neuropharmacological basis of impulsivity. Methylphenidate (Ritalin) and amphetamines are the primary medications for ADHD. Both block the reuptake of dopamine and norepinephrine into pre-synaptic neurons, which leads to an increase in post-synaptic levels of dopamine and norepinephrine. The increased availability of dopamine is considered a primary mechanism for the relief of ADHD symptoms. Thus, low dopaminergic tone has been suggested as one of the underlying neuropharmacological mechanisms of impulsive behavior. Similarly, increased noradrenaline has been shown to reduce impulsivity Motor loop Visual loop Motivational loop Executive loop Motor loop Motivational loop Executive loop Figure 8.2 Corticostriatal pathways. Disruptions in these pathways underlying executive, motivational, motor and visual function contribute toward impulsivity. (From Seger et al., 2011.) 116 Impulsivity

/ in widely utilized tasks of decision making such as the five-choice serial reaction time task (5CSRTT) and delay discounting tasks (Robinson et al., 2008). Some suggest that this may be an indirect effect that is based largely on downstream effects of noradrenaline on dopamine. Others, however, suggest a role of serotonin or 5-hydroxytryptamine (5-HT) levels in subcortical regions, such as the nucleus accumbens. This notion is based on studies demonstrating that an impulsive response to tasks such as the 5CSRTT is negatively correlated with 5-HT turnover in the nucleus accumbens (Moreno et al., 2010). Is Impulsivity Pre-existing or Drug Induced? Many consider impulsivity to be a continuous spectrum, and thus simply being impulsive does not, on its own, indicate pathology. However, impulsivity is more likely to be present in individuals with certain psychiatric disorders, such as addiction. Most studies that use self-reported measures of impulsivity find higher levels of impulsivity in substancedependent individuals than in healthy comparison subjects (Crews & Boettiger, 2009; Rodriguez-Cintas et al., 2016). Among substancedependent individuals, those who are dependent on multiple substances are more impulsive than those who are dependent on a single substance. Some of the most widely utilized self-report questionnaires are the Barratt Impulsiveness Scale (BIS-11), the UPPS-P Impulsive Behavior Scale (IBS) and the Kirby test of delay discounting, which yield three major subscales of impulsivity: “attentional,” “motor” and “non-planning.” The UPPS-P IBS is a fifty-nine-item self-reported scale withfive distinct subscales (positive urgency, negative urgency, lack of premeditation, lack of perseverance and sensation seeking). The idea that impulsivity may be a pre-existing vulnerability for addiction comes from work demonstrating the heritability of impulsivity as a stable trait (Kreek et al., 2005). One such study used a family study approach to determine the heritability of impulsivity. Ersche et al. (2010) examined impulsivity and sensation seeking in a large group of stimulant abusers and their siblings, as well as in age- and IQ-matched controls. As seen in Figure 8.3, impulsivity, but not sensation seeking, was significantly elevated in the siblings compared with controls, suggesting heritability of impulsivity. The stimulant-using individuals exhibited the highest levels of both sensation seeking and impulsivity. This is concordant with findings by de Wit (2009) showing that siblings of chronic stimulant users had higher levels of trait impulsivity than control volunteers, but did not differ from control volunteers with regard to sensation-seeking traits. Candidate gene Is Impulsivity Pre-existing or Drug Induced? 117

/ studies have also found associations between genes that regulate the serotonergic system (tryptophan hydroxylase 1 and 2, serotonin transporter), the dopaminergic system (dopamine transporter, monoamine metabolism pathway) and the noradrenergic system (dopamine β-hydroxylase) and impulsive personality. Together, these studies suggest that impulsivity is heritable and could be an endophenotype for addiction. Notably, the study by Ersche et al. (2010) also reported that those with stimulant abuse had impulsivity even greater than their siblings, suggesting that exposure to drugs may exacerbate an already elevated level of (d) (a) (b) (c) Impulsivity Controls BIS attention BIS motor BIS nonplanning Thrill and adventure seeking Experience seeking Disinhibition Boredom susceptibility Siblings Drug users Controls Siblings Drug users Controls BIS-11 total score (mean ±1 SE) SSS-V total score (mean ± 1 SE) Mean score ± 1 SE) Mean score (± 1 SE) Siblings Drug users Sensation seeking 100 80 60 40 20 0 30 25 20 15 10 5 0 10 8 6 4 2 0 40 30 20 10 0 * * * * * * * * * * Figure 8.3 Study in stimulant-dependent individuals, their non-using siblings and nonusing controls demonstrating that impulsivity traits (but not sensation seeking) may be a predisposing factor for stimulant dependence. The results show measurement of impulsivity traits using BIS-11 (a, c) and sensation-seeking personality traits using the SensationSeeking Scale Form V (SSS-V) (b, d). SE, standard error; *, significant difference at P<0.05. (From Ersche et al., 2010.) 118 Impulsivity

/ impulsivity. The notion that impulsivity may be drug induced comes from drug administration and neuroimaging studies. For example, there is considerable evidence that acute alcohol exposure increases impulsive responding in tasks such as the go/no go test and stop-signal reaction time (SSRT) task (Figure 8.4) (Dougherty et al., 2008). These widely Stimulus Duration Trial Go X X + Y + + X + Go Go No go Response Condition 1 700 ms 700 ms 700 ms 700 ms 300 ms 300 ms 300 ms 300 ms 2 3 4 312 Figure 8.4 Illustration of a go/no go test. A response is made for every go condition (i.e. each visual presentation of “X” and “Y”) but not for no go conditions (i.e. consecutive presentations of “X”). Is Impulsivity Pre-existing or Drug Induced? 119

/ used tasks of response inhibition measure one ’s ability to inhibit a motor response. Neuroimaging studies also demonstrate that chronic substance abuse is associated with structural, functional and metabolic changes in brain areas that underlie processes related to impulsivity, including the lateral prefrontal cortex (PFC) and orbitofrontal cortex. In sum, the neurotoxic effects of drugs on brain regions may underlie the impaired inhibitory processes observed in addiction. Given the mounting evidence suggesting impulsivity as a preexisting risk factor as well as a consequence of drug use, it is possible that these two etiologies both contribute to addiction, although at different stages of the process. Specifically, the existing relationship between impulsivity and other drug abuse vulnerability factors, such as sex, hormonal status, reactivity to non-drug rewards and early environmental experiences, may impact drug intake during all phases of addiction. Risky Decision Making Another aspect of impulsivity is acting without regard for consequences. Interestingly, while impulsivity often involves risks, the risks associated with impulsive behavior are often unrelated to sensation seeking, highlighting how impulsivity and sensation seeking are dissociable constructs (as described above in the study by Ersche et al., 2010). Support for this also exists in the animal literature. For example, in a study on rats differentially characterized on impulsivity and sensation seeking, it was found that high-sensation-seeking rats were more sensitive to cocaine and acquired cocaine selfadministration more rapidly compared with the high-impulsive rats that did not acquire cocaine self-administration as rapidly. However, the high-impulsive rats exhibited greater cocaine-seeking behavior despite mild foot-shock punishment (Belin et al., 2008). This drugseeking behavior despite negative consequences, in this case foot shock, is considered risky decision making. A widely utilized task to evaluate risky decision making in humans is the Iowa gambling task (IGT) (Bechara et al., 1994). The IGT is a computerized card game that measures sensitivity to rewards and losses. During the IGT, participants must weigh expected but uncertain rewards and penalties, for example taking bigger risks for greater rewards or smaller risks for lesser rewards. Using the IGT, neuroimaging studies have shown that the right ventromedial PFC is engaged during decision making, although activation in the left ventromedial PFC is associated 120 Impulsivity

/ with successful IGT performance. Lesion studies corroborate thesefindings, showing that those with ventromedial PFC lesions exhibit poor decision making. Other studies have also reported specificity of the ventromedial PFC’s role in decision making. A study by Clark et al. (2008) in lesion patients, for example, found dissociable roles for the ventromedial PFC and insula where the ventromedial PFC played a role in the regulation of decision making during trials with known outcome probabilities (see Figure 8.5), while the insula had a specific role only at more unfavorable odds, confirming the specificity of the insula during affective decision making. Inhibitory Control Another aspect of impulsivity is the ability to stop an action that has either already been initiated or is in the choice selection phase. Imagine the effort required to release the gas pedal when driving through a stoplight that has just turned from green to yellow. This action requires a similar process of inhibiting a pre-potent response (i.e. stepping on the gas pedal). As introduced earlier, some of the widely used tasks to measure inhibitory control are the SSRT task and the go/no go test. Whereas the SSRT involves the cancellation of an already selected response (“action cancellation”) the go/no go test implicates action restraint. An animal analogue of this paradigm is the 5CSRTT, where animals are trained to detect brief visual targets to earn food. Anticipatory responses that occur prior to the onset of the visual signals are considered premature responses. The circuit that underlies inhibitory control includes the right inferior frontal gyrus, the anterior cingulate cortex, and the presupplementary and motor cortex, as well as the basal ganglia and projections to the subthalamic nucleus (Aron et al., 2007) (Figure 8.6). Critics of this right-lateralized model argue for the additional contributions of left hemispheric regions. Some also suggest that, given that response inhibition during the SSRT task is in response to an external cue, the described processes may be predominantly attention driven. Last, despite the prevailing argument that inhibitory control is exerted top-down by cortical mechanisms, there is growing evidence that neural circuitry involving both cortical and subcortical mechanisms are implicated, particularly within the basal ganglia. Moreover, the possibility exists for impulsivity to be caused by chemical dysmodulation, not only of cortical processes but also at the level of the striatum. Inhibitory Control 121

/ 3 2 1 1 2 3 1 1 2 2 # of overlaps 3 4 5 > 3 VMPF VMPF IN Healthy controls 80 70 60 50 40 30 20 10 0 9 to 1 8 to 2 Chance of winning % Bet 7 to 3 6 to 4 Lesion controls VMPF Insula IN Figure 8.5 Ventromedial PFC lesions lead to risky decision making. A studies found that twenty patients with ventromedial PFC (VMPF) lesions (left side) exhibited greater betting behavior compared with forty-one non-lesion controls, thirteen patients with insula lesions 122 Impulsivity

/ Delay Discounting of Reward Preference of an immediately available small reward over experiencing a delay for a larger one is another facet of impulsivity referred to as delay discounting (Figure 8.7). Delay discounting can be modeled as hyperbolic discounting, originally described in pigeons that displayed a switch to selection of the smaller of the two rewards as their values decreased Figure 8.5 (cont.) and twelve lesion controls (with mainly dorsolateral and/or ventrolateral PFC damage). IN, insula cortex. (From Clark et al., 2008.) (A black and white version of this figure will appear in some formats. For the color version, please refer to the plate section.) “Stopping” impulsivity SNc PFC Caudateputamen Th dPM SMA/pre-SMA M1 GP STN LC Raphe RIFG/OFC ACC Figure 8.6 Schematic of the stop circuit. Inhibitory control depends on the interactions between PFC areas (cortical motor areas: M1, primary motor cortex; SMA/pre-SMA, supplementary motor area; dPM, dorsal pre-motor area), the right inferior frontal gyrus (RIFG), the anterior cingulate cortex (ACC), the orbitofrontal cortex (OFC), and striatal regions including the dorsal striatum (caudate-putamen), globus pallidus (GP) and subthalamic nucleus (STN), which project via the thalamus (Th) to the PFC. The PFC and striatal networks are modulated by midbrain dopaminergic neurons in the substantia nigra pars compacta (SNc)/ventral tegmental area, serotonergic neurons in the raphé nuclei (Raphe) and noradrenergic neurons in the locus coeruleus (LC). (From Dalley et al., 2011. © 2011 Elsevier, USA.) Delay Discounting of Reward 123

/ over time (Ainslie, 1975). Current delay discounting paradigms have measured choice after short temporal delays as well as probability discounting of reward where the dimension of temporal delay is replaced by reinforcer uncertainty. Smaller reward (immediate) E.g. $2 in 5 s E.g. $5 in 10 s $ $ $ $ Larger reward (delayed) Now Later Delay discounting task Figure 8.7 Illustration of a delay discounting task. “Waiting” impulsivity HC AMG ACC PLv PLd IL PFC VTA LC Raphe NAcb core NAcb shell Figure 8.8 Schematic of the wait circuit. Delay discounting of reward depends on top-down PFC interactions with the hippocampus (HC), amygdala (AMG) and structures in the ventral striatum, including the nucleus accumbens core (NAcb core) and shell (NAcb shell). The anterior cingulate cortex (ACC), dorsal and ventral prelimbic cortex (PLd and PLv), and infralimbic cortex (IL) make distinct contributions to waiting via topographically organized inputs to the NAcb. VTA, ventral tegmental area; LC, locus coeruleus. (From Dalley et al., 2011. © 2011 Elsevier, USA.) 124 Impulsivity

/ In contrast to inhibitory control as described above as the process of stopping a response, delay discounting can be viewed in terms of the action of waiting. A dissociation between inhibitory control (“stopping”) and delay discounting (“waiting”) is demonstrated in high-impulsive rats who exhibited delay discounting in the 5CSRTT although they had intact inhibitory control in the SSRT task. These findings suggest potentially two distinct neural substrates governing these impulsivity domains of stopping (e.g. dorsal striatum) versus waiting (e.g. ventral striatum) (Figure 8.8). One of the earliest relevant studies of delay discounting was the finding that rats that preferentially (75% of trials) chose small (two food pellets) immediate rewards over large (twelve pellets) rewards delivered after a delay of 15 s subsequently consumed significantly more of a 12% alcohol solution than the less-impulsive subgroups (Poulos et al., 1995). In terms of addiction, rats that demonstrate delay discounting acquire drug self-administration more quickly than rats that do not. Summary Points • Impulsivity is a heterogeneous construct consisting of independent processes that lead to poor decision making. • Although there is agreement that impulsive behavior is related to addiction, whether impulsivity is a cause or a consequence of addiction remains to be answered. It is also likely that, while impulsivity may be a risk factor that leads to addiction, drug exposure further exacerbates impulsive behavior, which leads to continued drug use. • Risky decision making is defined as persistence despite the potential for negative consequences. • Inhibitory control is the ability to inhibit a premature response. • Delay discounting of reward is preferential selection of immediate yet small rewards rather than waiting for delayed, larger rewards. • Corticostriatal networks underlie the various processes related to impulsivity. • Dopamine is the primary neurotransmitter that regulates impulsive behaviors, although both noradrenaline and serotonin also play a role. Review Questions • How can studies such as the one described by Erscheet al. (2010) decipher the chronicity of impulsive behavior in addiction? Review Questions 125

/ • What is the definition of risky decision making? • What are the most widely utilized paradigms to assess response inhibition? • What does delay discounting refer to? • How do corticostriatal regions interact to control behavior? • How do noradrenaline and serotonin contribute toward impulsive behavior? Further Reading Beaton, D., Abdi, H. & Filbey, F. M. (2014). Unique aspects of impulsive traits in substance use and overeating: specific contributions of common assessments of impulsivity. Am J Drug Alcohol Abuse, 40(6), 463–475. doi:10.3109/00952990.2014.937490 Crews, F. T. & Boettiger, C. A. (2009). Impulsivity, frontal lobes and risk for addiction. Pharmacol Biochem Behav, 93(3), 237–247. doi:10.1016/j. pbb.2009.04.018 Ding, W. N., Sun, J. H., Sun, Y. W.,et al. (2014). Trait impulsivity and impaired prefrontal impulse inhibition function in adolescents with internet gaming addiction revealed by a Go/No-Go fMRI study.Behav Brain Funct, 10, 20. doi:10.1186/1744-9081-10-20 Filbey, F. M. & Yezhuvath, U. S. (2017). A multimodal study of impulsivity and body weight: integrating behavioral, cognitive, and neuroimaging approaches.Obesity (Silver Spring), 25(1), 147–154. doi:10.1002/oby.21713 Filbey, F. M., Claus, E. D., Morgan, M., Forester, G. R. & Hutchison, K. (2012). Dopaminergic genes modulate response inhibition in alcohol abusing adults. Addict Biol, 17(6), 1046–1056. doi:10.1111/j.1369-1600.2011.00328.x Hu, Y., Salmeron, B. J., Gu, H., Stein, E. A. & Yang, Y. (2015). Impaired functional connectivity within and between frontostriatal circuits and its association with compulsive drug use and trait impulsivity in cocaine addiction. JAMA Psychiatry, 72(6), 584–592. doi:10.1001/jamapsychiatry.2015.1 Jupp, B. & Dalley, J. W. (2014). Convergent pharmacological mechanisms in impulsivity and addiction: insights from rodent models. Br J Pharmacol, 171(20), 4729–4766. doi:10.1111/bph.12787 McHugh, M. J., Demers, C. H., Braud, J.,et al. (2013). Striatal-insula circuits in cocaine addiction: implications for impulsivity and relapse risk.Am J Drug Alcohol Abuse, 39(6), 424–432. doi:10.3109/00952990.2013.847446 Pivarunas, B. & Conner, B. T. (2015). Impulsivity and emotion dysregulation as predictors of food addiction. Eat Behav, 19, 9–14. doi:10.1016/j. eatbeh.2015.06.007 126 Impulsivity

/ Stevens, L., Verdejo-Garcia, A., Goudriaan, A. E.,et al. (2014). Impulsivity as a vulnerability factor for poor addiction treatment outcomes: a review of neurocognitive findings among individuals with substance use disorders. J Subst Abuse Treat, 47(1), 58–72. doi:10.1016/j.jsat.2014.01.008 Winstanley, C. A. (2007). The orbitofrontal cortex, impulsivity, and addiction: probing orbitofrontal dysfunction atthe neural, neurochemical, and molecular level. Ann N Y Acad Sci, 1121, 639–655. doi:10.1196/annals.1401.024 Spotlight Why So Impulsive? Teenagers are universally viewed as an impulsive population. Before the advent of imaging technology, it wasthoughtthat, following puberty, individuals(and their brains) are more orless how they will be forthe rest oftheirlives. However, research has shown that the teenage brain is still developing, with areas for impulse control and decision making– the PFC – being the last to develop (Figure S8.1). The brain, in essence, develops from the back to the front. Figure S8.1 Adolescence is a critical neurodevelopmental period and is associated with highly impulsive behavior. Spotlight 127

/ These longitudinal studies collecting structural brain data on individuals across multiple years during adolescent development noted that the brain continues to develop into the mid- to late-20s before it is considered fully“mature” or fully myelinated to adult levels. The critical neurodevelopment during this period occur within the white matter tracts that connect different brain regions. Thus, these frontal control areas are not accessed asrapidly. Thisleads to greater risk-taking behavior, including substance use. References Ainslie, G. (1975). Specious reward: a behavioral theory of impulsiveness and impulse control. Psychol Bull, 82(4), 463–496. doi:10.1037/h0076860 Aron, A. R., Behrens, T. E., Smith, S., Frank, M. J. & Poldrack, R. A. (2007). Triangulating a cognitive control network using diffusion-weighted magnetic resonance imaging (MRI) and functional MRI. J Neurosci, 27(14), 3743–3752. doi:10.1016/0010-0277(94)90018-3 Bechara, A., Damasio, A. R., Damasio, H. & Anderson, S. W. (1994). Insensitivity to future consequences following damage to human prefrontal cortex. Cognition, 50(1–3), 7–15. Belin, D., Mar, A. C., Dalley, J. W., Robbins, T. W. & Everitt, B. J. (2008). High impulsivity predicts the switch to compulsive cocaine-taking. Science, 320(5881), 1352–1355. doi:10.1126/science.1158136 Clark, L., Bechara, A., Damasio, H., et al. (2008). Differential effects of insular and ventromedial prefrontal cortex lesions on risky decisionmaking. Brain, 131(5), 1311–1322. doi: 10.1093/brain/awn066 Crews, F. T. & Boettiger, C. A. (2009). Impulsivity, frontal lobes and risk for addiction. Pharmacol Biochem Behav, 93(3), 237–247. doi:10.1016/j. pbb.2009.04.018 Dalley, J. W., Everitt, B. J., & Robbins, T. W. (2011). Impulsivity, compulsivity, and top-down cognitive control. Neuron, 69(4), 680–694. doi:10.1016/j.neuron.2011.01.020 de Wit, H. (2009). Impulsivity as a determinant and consequence of drug use: a review of underlying processes. Addict Biol, 14(1), 22–31. doi:10.1111/j.1369-1600.2008.00129.x Dougherty, D. M., Marsh-Richard, D. M., Hatzis, E. S., Nouvion, S. O. & Mathias, C. W. (2008). A test of alcohol dose effects on multiple behavioral measures of impulsivity. Drug Alcohol Depend, 96(1–2), 111–120. doi:10.1016/j.drugalcdep.2008.02.002 Ersche, K. D., Turton, A. J., Pradhan, S., Bullmore, E. T. & Robbins, T. W. (2010). Drug addiction endophenotypes: impulsive versus sensation128 Impulsivity