Substance use disorders: a theory-driven approach to the integration of genetics and neuroimaging

Transcription

1 Ann. N.Y. Acad. Sci. ISSN ANNALS OF THE NEW YORK ACADEMY OF SCIENCES Issue: Addiction Reviews : a theory-driven approach to the integration of genetics and neuroimaging Hollis C. Karoly, Nicole Harlaar, and Kent E. Hutchison Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, Colorado Address for correspondence: Hollis C. Karoly, Department of Psychology and Neuroscience, University of Colorado at Boulder, 345 UCB, Room D324, Boulder, CO hollis.karoly@colorado.edu The etiology of substance use disorders is related to changes in neuronal systems involved in reward anticipation, negative affect, and withdrawal, as well as to alterations in inhibition and executive control. Genetic and epigenetic variation associated with individual differences in these mechanisms may be important for predicting the effectiveness of current treatments and informing future pharmacogenomic investigations. Genetic research efforts have increasingly involved the use of approaches that leverage neurobiological phenotypes to link changes at the molecular level (e.g., genetic and epigenetic variation) to changes in intermediate neuroimaging phenotypes, and ultimately to clinical outcomes. The current review summarizes recent efforts that utilize neuroimaging and genetic approaches in the context of a three-stage model of addiction. In addition, this review explores how these approaches have been used to study the progression from impulsive, recreational substance use to the compulsive, addicted state. Finally, this review describes future ways that research may incorporate these approaches to examine important stage-specific mechanisms of addiction. Keywords: neurobiology; addiction; genetics; epigenetics; neuroimaging; alcohol Introduction (SUDs) are chronic neurobiological disorders characterized by the compulsion to seek and use a substance, the loss of control over consumption, and the emergence of a negative emotional state when access to the substance is prevented. 1 One recent estimate suggests that 18% of Americans will experience an alcohol use disorder (AUD) during their lifetime, 2 at a yearly healthcare cost of approximately $235 billion. 3 In 2009, there were almost 4.6 million drug-related emergency room visits nationwide, with nearly a million of those involving an illicit drug. 4 Illicit drug use rates in 2009 were at the highest level since 2002, and continued to increase into Because of the substantial burden they impose on society, SUDs are a serious public health concern. Although empirically supported treatments for SUDs are available, 6 treatment efficacy remains relatively modest. More effective treatment approaches will require advances in our understanding of the neurobiological mechanisms that underlie development and maintenance of SUDs. Approaches to the treatment of SUDs may also benefit from a greater understanding of the genetic and epigenetic factors that exacerbate or mitigate the neuronal adaptations underlying the etiology and progression of these disorders. 7 To facilitate a translational approach to the study of these factors, this review advances a theory-driven integration of neuroimaging, genetic, and epigenetic approaches to the study of addiction. The theoretical framework for this integration is based on a combination of recent conceptual models of how the brain changes during the progression of SUDs, emphasizing an allostatic model 1, 8, 9 of SUD development in three stages: (1) binge/intoxication, (2) withdrawal/negative affect, and (3) preoccupation/anticipation. Other models of addiction emphasize a balance between the brain s incentive motivation network involved in reward anticipation and the control network implicated in selfregulation and control over compulsive actions such doi: /nyas Ann. N.Y. Acad. Sci (2013) C 2013 New York Academy of Sciences. 71

2 as drug taking. 10, 11 Additionally, other theoretical approaches emphasize intermediate, translational, and neurobiological phenotypes linking molecular (e.g., genetic and epigenetic) changes to neuronal functioning indicated by blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fmri) response, as well as white matter integrity or network connectivity and clinical measures that reflect the balance or imbalance of these networks. 7 We propose an integration of these models in a way that enables and encourages a stage-based application of research designed to identify genetic and epigenetic loci based upon functional associations with component processes within each of the three stages (Fig. 1). In this integrated framework, the stages of the addiction cycle represent a means for organizing research efforts to identify neurobiological phenotypes that reflect the progression of neuronal adaptations and identify genetic and epigenetic correlates that mitigate or exacerbate these adaptations. Thus, this integrated model may provide a more suitable lens through which to view the complex problem of addiction and identify biomarkers with prognostic value. In general, the proposed framework utilizes a 1, 8, 9 three-stage organization of an allostatic model that places emphasis on balance and connectivity between reward/incentive salience and control networks found in other models (see Fig. 1). 7, 12 For example, at the binge/intoxication stage, during which individuals initially administer drugs in order to obtain pleasurable effects, there is dysregulation of the brain networks that underlie reward; connections to the ventral pallidum (VP) and dorsal striatum (DS), as well as nucleus accumbens (Nacc) and amygdala are predominantly implicated During this stage, the network that underlies reward and incentive salience becomes more dominant, while the network underlying control begins to weaken and the withdrawal/negative affect component remains relatively dormant (see Fig. 1A). These adaptations begin early in the substance use trajectory, often during adolescence, and may be more likely to occur in individuals at high risk for SUDs owing to genetic background, family history, and/or personality factors. It is important to emphasize that most individuals who use substances do not develop disordered patterns of use. For example, heavy drinking generally increases in late adolescence, peaks around age 22, and declines to nonproblematic levels with age; 19 this maturing out phenomenon may be due to changes in personality, 20 life circum- 21, 22 stances, or increasing levels of responsibility. For those who go on to develop SUDs, however, the binge/intoxication stage generally leads to the withdrawal/negative affect stage. The withdrawal/negative affect stage is characterized by negative emotional states such as chronic irritability, emotional distress, malaise, dysphoria, alexithymia, loss of motivation for natural rewards, 23 and concurrent recruitment of stress neurocircuitry. 24 During this stage, the reward/incentive salience network is relatively strong, while the control network is fairly weak (see Fig. 1B). The preoccupation/anticipation stage is dominated by persistent substance craving and disrupted inhibitory control, and likely involves dysfunction in the glutamate-mediated medial prefrontal cortex (mpfc)/nacc/ventral pallidum circuit, 25 and basolateral amygdala. 26, 27 During this stage, connections between the reward/incentive salience network and the DS, as opposed to the ventral striatum (VS), become strong. Connectivity with the control network becomes weak, and substance use becomes habitual (see Fig. 1C). Within this theoretical context, the following review summarizes the existing literature on the neurobiological mechanisms involved in the stages of the addiction cycle and discusses potential genetic markers associated with stage-specific mechanisms, expanding on a translational model linking genetic variation to the neurobiological underpinnings of substance dependence. 7 We propose that incorporating genetic information into this modified allostatic model of addiction is useful for informing future research and for identifying biomarkers that predict the course of addiction or the success of different treatment options. Given the increasing number of neuroimaging and genetic studies of substance addiction conducted in recent years, this review will not present an exhaustive review of the entire body of literature. Rather, it focuses on a selection of recent studies, emphasizing how these phenotypic measures fit within the theoretical framework presented above. We will primarily concentrate on studies of alcohol dependence (AD) and nicotine dependence (ND), as these substances have been the most extensively examined in neuroimaging and genetic research to 72 Ann. N.Y. Acad. Sci (2013) C 2013 New York Academy of Sciences.

3 Figure 1. Relationships among genetic variation, brain regions, and neural networks influencing aspects of each of the three stages of the addiction cycle. (A) In the binge/intoxication stage, drug use is primarily driven by impulsive motivation and positive reinforcement. Both the incentive reward and control networks are active. However, repeated substance use leads to a slight Ann. N.Y. Acad. Sci (2013) C 2013 New York Academy of Sciences. 73

4 date. Additionally, given the number of functional neuroimaging tasks currently being used to study psychological disorders, the present review was unable to mention every relevant task. The following thus focuses primarily on fmri tasks used most often in substance abuse research. This review most strongly emphasizes tasks that have been informative for gaining a better understanding of important neurobiological substance use phenotypes. Neuroimaging studies and the stages of addiction The use of structural and functional MRI has increasingly aided the investigation of neural structure, function, development, and disease/disorder. Functional MRI methods, widely applied to the study of psychiatric disorders since the mid-1990s, serve as a useful but indirect way to visualize functional neural changes because it measures changes in oxygen content in the blood while subjects are engaged in a task. When activation in a certain brain area increases, blood flow and oxygen content in the blood in that area increases. The signal measured in fmri is due to changes in blood flow: as a greater amount of oxyhemoglobin is carried into a particular region, a decrease in deoxyhemoglobin in the blood occurs, and it is this decrease that is detected by fmri technology. 28 Over the last two decades neuroimaging has been used extensively to examine structural and functional brain mechanisms underlying addiction. We will discuss the application of neuroimaging modalities for studying the disturbance in motivation and reward systems that are characteristic of the binge/intoxication stage, the negative affect and loss of control associated with the withdrawal/negative affect stage, and the craving, drug anticipation, and self-regulatory difficulties present in the preoccupation/anticipation stage. Although the three-stage model does not technically encompass at-risk individuals before initiation of substance use, we briefly note the promising use of neuroimaging modalities to characterize such individuals for developing SUDs. At present, considerable fmri evidence suggests that adolescents at risk for developing later substance use problems display characteristic brain responses in the context of particular functional tasks. For example, in a study of youth with familial substance use risk, high neurobehavioral disinhibition scores compiled from several behavioral and neuropsychological tests predicted decreased activation in frontal, but not parietal, occipital, or temporal, brain regions in response to a task requiring inhibition of eye movements. 29 In other fmri studies, youths with a family history of alcoholism showed less inhibitory frontal response to a Go/No Go paradigm compared with those with no family history. 33 Less neural activity during response inhibition in a Go/No Go task has been found to predict future substance use, 34 and adolescents with potential substance use problems showed greater risk-taking and lower striatal activation than controls in a gambling task. 35 Youths with a family history of substance abuse also appear to have disrupted white matter microstructure. 36 Thus, underlying risk factors, such as disinhibition and impaired impulse control, are other important targets of neuroimaging research aimed at better understanding the neurobiology of addiction. In particular, neuroimaging studies of at-risk individuals before initiation of substance use would help to disentangle the neurobiological factors that predispose certain imbalance in which structures implicated in the control network are overpowered by brain regions involved in the incentive/reward pathway, thus perpetuating substance use. Premorbid genetic factors, represented by G 1 and G 2, also contribute to this imbalance. G 1 and G 2 are genetic composites, each consisting of hundreds, and potentially, thousands of genetic and epigenetic variants. The network connecting key brain reward areas, such as the VTA, Nacc, thalamus, and insula, is strengthened with repeated drug use during this stage. (B) As the addiction cycle progresses to the withdrawal/negative affect stage, repeated drug use leads to an increase in connectivity between regions involved in withdrawal/negative affect, particularly within the extended amygdala. These adaptations influence both the reward and control networks, leading to a downstream increase in strength of the reward network and decrease in strength of the control network. Both genetic and epigenetic factors (represented by G 3 ) influence the activation of withdrawal/negative affect regions, and its subsequent effects on the incentive reward and control networks. (C) By the time an individual has reached the preoccupation/anticipation stage, drug use is driven by compulsive motivation (rather than impulsive motivation, as in stage 1) and negative reinforcement. The strength of the incentive reward network has been continually increased throughout the cycle and now significantly overpowers the control network. The cingulate, insula, basolateral amygdala, and hippocampus are brain areas most strongly implicated in the craving experienced once an individual has moved into the preoccupation/anticipation stage. 74 Ann. N.Y. Acad. Sci (2013) C 2013 New York Academy of Sciences.

5 vulnerable individuals to develop SUDs from the brain changes that occur as a result of acute or prolonged substance use. The binge/intoxication stage The binge/intoxication stage involves disturbances in brain networks underlying reward, motivation/drive, and salience attribution. 9, 12 Studies over the past 25 years have shown that drugs of abuse generally exert their initial reinforcing effects by inducing sharp increases in dopamine in limbic brain regions, subsequently leading to alterations in other neurotransmitter systems and reward responsivity. 8, 12, The brain changes that occur in response to administration of drugs of abuse over time are malleable and susceptible to alterations but tend to persist in individuals when administration of drugs is continued. This persistence is related to dysfunction in reward responsivity, as adaptations in dopaminergic circuits activated by drugs of abuse cause individuals to become more responsive to the dopaminergic increases produced by their drug of choice, and less responsive to the increases in dopamine produced by naturally rewarding reinforcers, 43 leading to an overvaluation of drugrelated rewards and persistent drug-seeking behavior. The disturbance in behavioral regulation and overvaluation of drug-related reward stimuli begins during the binge/intoxication stage and continues as the individual progresses toward dependence. It is also important to note that the adaptations occurring at this stage generally take place in adolescence and early adulthood, during which initiation of substance use commonly occurs. While most of the initial work on neurobiological mechanisms underlying the binge/intoxication stage was done using animal models, 44 extensive fmri research has examined the recruitment of various brain areas after exposure to drug-related cues (e.g., tastes, pictures, words) in humans. Consistent with animal models, the results from these studies indicate that SUDs involve an overvaluation of the incentive salience of drugs. 45 Studies examining brain activation after exposure to alcohol versus neutral cues have shown that the putamen, anterior cingulate cortex (ACC) and adjacent mpfc are activated in alcohol-dependent compared to control subjects. 46 A similar study showed that, compared with control subjects, alcohol-dependent subjects demonstrate activation in the prefrontal and cingulate cortex, as well as in the precuneus and adjacent parietal cortex, that is significantly greaterforalcoholversusneutralcues. 48 Activation in the left Nacc, ACC, and left orbitofrontal cortex (OFC) in response to alcohol cues significantly correlates with subjective craving for alcohol among alcohol-dependent, but not control subjects. 47 In addition, alcohol-dependent women showed increased activation in subcallosal, anterior cingulate, left prefrontal, and bilateral insular regions in response to alcohol-related compared to neutral words. 49 Relatedly, cue-exposure studies conducted among cigarette smokers have demonstrated significant differences in brain activation in response to smoking versus control cues Robust changes in activation were found in the dorsal ACC, posterior cingulate cortex (PCC), and precuneus when subjects were instructed to resist the urge to crave cigarettes in response to cigarette cues, compared with subjects who were instructed to allow themselves to have cravings in response to cues. 53 Additionally, in cocaine users, exposure to visual cocaine cues elicits increased limbic activation, particularly in the amygdala and anterior cingulate, 54 and intravenous administration of cocaine activates mesolimbic and mesocortical dopaminergic projection regions. 55 Further, the intensity of activation in brain areas responsive to cocaine-related cues is correlated with the strength of drug-craving. 56 In addition to cue-exposure paradigms, tasks targeting the balance between assessment of reward and risk, as well as the ability to delay reinforcement/reward (e.g., balloon analog risk task (BART 57 ), delay discounting, 58 and monetary incentive delay (MID 59 )) are relevant for assessing changes within the reward and control networks during the binge/intoxication stage. Studies using BART have associated risk-taking with increased activation in the dorsal ACC and anterior insula, and severity of alcohol problems with BOLD response in the dorsal ACC. 60 Using the delay discounting task, severity of alcohol-related problems predicts increased discounting of delayed rewards, and is associated with greater activation in supplementary motor area, insula/ofc, inferior frontal gyrus, and precuneus. 61 Immediate reward bias is associated with activation within posterior parietal cortex, dorsal PFC, and rostral parahippocampal gyrus, whereas the tendency to wait for larger, Ann. N.Y. Acad. Sci (2013) C 2013 New York Academy of Sciences. 75

6 delayed rewards correlates with activation in the lateral OFC. 62 In adolescents studied using MID, risktaking bias is related to decreased activation in the VS during reward anticipation. 35 Finally, the Go/No Go paradigm has been used to target response inhibition, a key element in the early stages of the addiction process. During performance in the Go/No Go task, acute alcohol administration impairs error processing, 63 and alcohol intoxication is associated with more false alarm responses to No Go targets, but only in individuals with low working memory. 64 Further, higher impairment of inhibitory control during this task in response to acute alcohol intoxication was related to higher levels of ad-libitum alcohol consumption during a single drinking episode in the laboratory. 65 The inhibitory control impairment observed in response to No Go targets after moderate doses of alcohol is generally not accompanied by impairments in responding to the Go targets, suggesting that alcohol-induced disruption to inhibitory control is selective, and is not a result of global decreases in psychomotor performance. 66 During cannabis intoxication, electrocortical activity in response to the Go/No Go task has also been shown to be severely disrupted. 67 Overall, the Go/No Go task illuminates the difficulties in inhibitory control related to acute substance intoxication. The withdrawal/negative affect stage During the withdrawal/negative affect stage, negative emotional states weaken the control network and simultaneously increase the strength of connections in the reward network. This imbalance ultimately leads to drug-seeking behavior driven by negative reinforcement. Exploring the neurobiological underpinnings of this stage is important for understanding the maintenance of addiction and individual differences in habitual substance use. Several studies suggest that affective cues, including stress exposure using personalized stress-scripts, 68 moderate the response of networks that underlie both reward and cognitive control. For example, in a study comparing individualized script-driven stress imagery with personalized script-driven neutral imagery during stress exposure, cocaine-dependent patients failed to activate regions associated with the control and regulation of emotion and distress. Rather, they exhibited greater craving-related activation in the dorsal striatum. 69 Among smokers exposed to the Montreal Imaging Stress Task (MIST), 70 psychosocial stress exposure was associated with limbic deactivation that predicted subsequent neural responses to smoking cues, suggesting that stress increases the incentive salience of drug cues. 71 This relationship could be further explored among smokers who are experiencing acute or protracted withdrawal, compared with smokers who areallowedtosmokeasusual. Additionally, withdrawal from alcohol is associated with altered functional neural connectivity during emotional processing. 72 Inordertomore specifically target the neural correlates underlying processing of negative affect in addiction, activation in response to emotional faces could be compared between individuals experiencing acute or protracted withdrawal and those not undergoing withdrawal. 51 In an fmri study, presenting threatening and nonthreatening facial stimuli during intravenous alcohol administration showed that alcohol robustly activated striatal reward circuits while attenuating responses to fearful stimuli in visual and limbic regions. 73 These results serve to illuminate the neurobiological mechanisms underlying alcohol s anxiolytic, stress-reducing effects. Further, a series of fmri studies showed that medial prefrontal and anterior cingulate regions are involved in processing emotional distress, but that addicted subjects show dysfunction in distress processing (i.e., decreased emotional regulation and control, and increases in habits/compulsions during stress), which likely underlies the vulnerability to stress-induced, craving-related relapse observed in substance-dependent individuals. 74 Similarly, fmri results in abstinent alcoholics show hyperresponsivity of striatal-limbic regions associated with emotional processing, and hyporesponsivity in medial frontal regions associated with emotional regulation, self-control, and executive functioning. 75 On a methodological note, it is important to consider that recently abstinent individuals who participate in fmri scans may still be undergoing protracted withdrawal, which could be reflected in their functional and structural images. To address this issue, it may be important to study individuals who have been abstinent for a longer period of time. 76, 77 This is a useful approach for disentangling residual drug effects from longer-lasting brain and behavioral changes due to chronic substance-use disorders. 76 Ann. N.Y. Acad. Sci (2013) C 2013 New York Academy of Sciences.

7 Among smokers, withdrawal processes also seem to influence networks involved in reward and control. Greater activation was found in response to smoking cues compared to control cues in parietal, frontal, occipital, and central cortical regions, as well as in the dorsal striatum and thalamus, following 24 hours of nicotine abstinence, but no differences in activation existed between smoking and control cues after smoking as usual. 78 These findings are consistent with the theoretical conceptualization presented in Figure 1, as they support the role of acute withdrawal in differentially biasing reward and control networks in response to drug cues. Additionally, subjective nicotine withdrawal symptoms reduce thalamic blood flow, but not ventral striatal blood flow, while nicotine administration lead to increased ventral striatal blood flow. 79 This suggests that distinct brain systems moderate the aversive aspects of withdrawal and the acute effects of reward. The preoccupation/anticipation stage Gaining a deeper understanding of the preoccupation/anticipation, or craving, stage of addiction, characterized by compulsive drug-seeking behavior, will be useful in developing more effective treatment and relapse prevention. This stage can be considered the end product of long-term neurobiological adaptations that occur during the previous stages. Recently, the preoccupation/anticipation stage has been explored in clinical populations. Some of the same neurocognitive tasks used to study changes in reward salience relevant to the binge/intoxication stage have also proven effective for investigating preoccupation/anticipation, given that reward responsiveness and inhibitory control continually become more impaired as the addiction cycle progresses. However, a major difference during preoccupation/anticipation compared to binge/intoxication is a greatly exaggerated response to the presentation of drug cues with concurrent decreases in response to natural rewards. For example, alcohol-dependent individuals compared to control subjects demonstrated reduced activation in the VS during expectation of monetary reward, and increased activation during presentation of alcohol cues. 80 Another key difference is that binge/intoxication is primarily studied in adolescent and young adult subjects, whereas preoccupation/anticipation research tends to focus on older, heavily dependent individuals. Cognitive deficits in this stage are prevalent, and may be closely related to risk for relapse and/or poor treatment outcomes. 81 The dopamine released when addictive drugs are taken acts as a learning signal, short circuiting the normal controls of dopamine release. 82 Because of the resulting increase in salience of drug-related rewards, adaptive aspects of cognition, particularly response inhibition, are weakened. Thus, addicted individuals become overly responsive to drug cues and experience difficulty in controlling drug-seeking behavior, despite negative consequences related to drug use. Unlike BART, delay discounting, and MID, the Go/No Go task targets response inhibition/cognitive control but lacks a reward/risk component, and has also been used for studying risk for substance use and activation patterns present at the binge/intoxication stage. Using this task, significant hypoactivity was observed in the cingulate, insula, and presupplementary motor areas during successful inhibition and errors of commission in chronic cocaine users relative to controls. 83 Opiate addicts exhibit an attenuated ACC signal during errors on the Go/No Go, and significantly poorer overall performance than controls. 84 Another task useful for examining cognitive control in long-term substance users is the MSIT, 85 which uses a response-conflict paradigm involving interference from several different tasks engaging competing response tendencies, in order to place high demands on multiple areas of cognitive control. Research using this task has shown that longterm opiate users require increased recruitment of fronto-parietal and cerebellar networks underlying behavioral regulation in order to achieve normal levels of task performance/behavioral control. 86 Finally, the Stroop color-word interference task has been used to study substance-dependent individuals. 87 This task is intended to measure an individual s ability to monitor and inhibit automatic processes in order to perform a selected task despite being presented with competing, interfering information (i.e., naming the ink color of the word blue that is printed in red ink). Activation in the dorsolateral prefrontal cortex, striatum, and cingulate cortex in response to the Stroop task has been found to predict treatment outcomes in cocaine-dependent patients. 88 This task can be modified to target individuals dependent on particular substances by incorporating words related to that substance into Ann. N.Y. Acad. Sci (2013) C 2013 New York Academy of Sciences. 77

8 the task presentation. For example, among smokers, a processing bias was found for smoking-related words. 89 During preoccupation/anticipation, addicted individuals are vulnerable to reinstatement induced by drug cues- and/or stress. In an fmri study of cue-elicited craving in cannabis users, the ventral tegmental area (VTA), thalamus, ACC, insula, and amygdala (all components of the brain s reward pathway), demonstrated greater activation in response to a marijuana cue relative to a neutral cue. 90 On the other hand, stress-induced reinstatement requires activation within the extended amygdala, namely the central nucleus of the amygdala and bed nucleus of the stria terminalis. 91 Altered function in medial prefrontal, anterior and posterior cingulate, as well as striatal and posterior insula, have been found to be related to relapse outcomes as well as to stress- and drug-induced craving states. 74 Gaining a better understanding of both cue- and stress-induced craving during the preoccupation/anticipation stage may be necessary in order to effectively predict individual propensity to relapse. Finally, neuroimaging research examining resting-state connectivity among substance users is particularly relevant in targeting the later stages of addictive disorders, given that it is well known that long-term substance use leads to abnormal functional organization of the brain. Resting-state functional connectivity is a measure of the synchrony of activity between brain regions, assessed through correlating spontaneous fluctuations in BOLD signals across different regions of the resting brain using fmri. Resting-state connectivity, or resting-state synchrony (RSS), is thought to be a measure of communication between brain regions and functional organization within the brain. 92 Among alcohol-dependent individuals in early abstinence, individuals who later relapsed showed significantly decreased RSS within both the neural reward and executive control networks compared to those who remained abstinent, 93 suggesting that lower RSS during the initial stages of abstinence may predict later relapse. Additionally, individuals in long-term abstinence from alcohol showed decreased RSS in limbic reward areas, with increased RSS in executive control regions, suggesting the presence of an adaptive mechanism among these long-term abstinent individuals allowing them to maintain abstinence through facilitation of behavioral control. 94 Resting-state connectivity also appears to be altered among users of other addictive substances including heroin 95, 96 and cocaine. 97 In general, resting-state measures provide useful information regarding stable neural characteristics without being confounded by aspects unique to individual functional tasks; thus, resting-state data can be more easily compared across different study designs. For this reason, resting-state studies of long-term substance users could serve as an effective means for elucidating abnormal functional connectivity throughout the stages of addiction. Genetics and the stages of addiction There is a strong genetic component underlying the risk for developing SUDs, with heritability estimates for addiction from twin and adoption studies falling at approximately 30 60%. 98 Twin studies have revealed that, in early adolescence, the initiation and use of nicotine, alcohol, and cannabis are more strongly determined by familial and social factors, but these gradually decline in importance during the progression to young and middle adulthood, when the effects of genetic factors become maximal, declining somewhat with age. 99 Regardless of when SUDs become manifest, genetic factors appear to contribute to all stages of the cycle. This research has catalyzed and informed studies seeking to identify specific genes associated with SUDs. 100 Generally, studies attempting to link specific genetic variations to risk for SUDs have taken one of two approaches. Genome-wide association studies (GWAS) test hundreds of thousands of genetic variants across the genome in one study. This approach is advantageous in that, given sufficient sample sizes coupled with meaningful phenotypes, it can detect associations of common variants in the absence of a priori hypotheses. GWAS generally require genotyping of case subjects possessing the disease of interest as well as healthy control subjects and comparing them for frequencies of common single nucleotide polymorphisms (SNPs), differences that vary in at least 5% of the population across the genome. Alternatively, GWAS can examine quantitative traits. To date, the most successful GWAS results have come from the analyses of ND that have yielded consistent and robust evidence for associations with a region on the long arm of chromosome 15 (15q25) 78 Ann. N.Y. Acad. Sci (2013) C 2013 New York Academy of Sciences.

9 that contains the 5, 3, and 4 nicotinic acetylcholine receptor subunit gene cluster, CHRNA5- CHRNA3-CHRNB4(CHRNA5-A3-B4), notably for the rs and rs SNPs within this gene cluster. 101 These SNPs are also associated with objective measures of tobacco exposure. 102 Further, a large consortium study showed that rs meets genome-wide significance for daily cigarettes consumed. 103 Within 15q25, a meta-analysis showed that rs within the promoter region of CHRNA5 had the highest association with smoking-related behaviors. 104 Additional SNPs have reached nominal significance at the genome-wide level using smoking cessation success as the phenotype. 105 Results from GWAS of other SUDs have been mixed. For example, six independent GWAS regarding AD have yielded a few significant hits, including significant associations with SNPs in the alcohol dehydrogenase (ADH) gene cluster, 106 the aldehyde dehydrogenase (ALDH2) gene, 107 and the PECR gene. 108 However, a striking feature of the evidence to date is the general lack of replication of significant SNPs across studies. This trend has been attributed to (1) each individual SNP likely has a very small effect; (2) a lack of statistical power due to multiple testing correction -meaning that genome-wide significance thresholds tend to be in the order of ; and (3) rare variations is not covered very well on the arrays (see Ref. 98 for a review). Alternative experimental designs to GWAS are candidate gene studies. These focus on gene variants in specific genomic regions, and often target genes coding for proteins known to be involved in the addiction process. For example, one of the most consistently replicated genetic polymorphisms for AD is the A1 allele of the Taq1A polymorphism (rs ) of thedrd2gene, a C > T substitution located in a noncoding region of thedrd2locus. 109 Other research indicates that risk for SUDs involves genetic polymorphisms in other genes involved in the serotonin, norepinephrine, endorphin and opioid receptors, as well as dynorphin, gamma-aminobutyric acid (GABA), endocannabinoids, neuropeptide Y (NPY), galanin, orexin, substance P, melanocortins, leptin, glutamine, and glucocorticoids, among other neurochemical systems. 110 Many of these studies have identified genetic variations that appear to influence reward responsiveness or particular neurotransmitter systems within reward circuitry that are affected by specific addictive substances, and may explain some of the individual differences in the binge/intoxication phase of the addiction process Studies combining neuroimaging and genetics The binge/intoxication stage Neuroimaging methods have been combined with genetic analyses to identify informative neurobiological phenotypes and underlying genetic vulnerabilities. These studies have primarily taken candidate gene approaches. For example, studies targeting the binge/intoxication stage have demonstrated the effects of various polymorphisms that influence dopaminergic systems on frontostriatal activity during reward processing One recent study found that a polymorphism in the CNR1 gene is associated with greater alcohol cue-elicited brain activation in the midbrain and PFC. 86 Also, the DRD4 VNTR and OPRM1 A118G polymorphisms were associated with functional neural changes in mesocorticolimbic structures after exposure to alcohol cues, 122 and the DRD4 VNTR polymorphism in particular has been shown to correlate with response to smoking cues in brain areas mediating executive and somatosensory processes. 123 A multilocus genetic composite comprising dopamine gene variations was found to significantly influence reward processing in the VS independent from substance abuse. 124 Another multilocus genetic composite related to dopamine signaling predicted decreased activation in reward-related regions. 125 Further, COMT Val158Met and DRD2 Taq1A polymorphisms may confer risk of AD via reduced dopamine receptor sensitivity in the PFC and hindbrain, respectively. 126 These results demonstrate the promise of biologically informed, multilocus genetic composites for predicting individual differences in behaviorally relevant neural function. GABRA2 has also been implicated in the risk for developing AD, and association analyses demonstrated that GABRA2 could influence vulnerability to AUDs through modulation of neural excitation levels. 127 GABRA2 may also affect neural responses to alcohol cues, via the mesocorticolimbic reward circuitry. 128 In a family sample enriched for alcoholism, increased insula activation during reward, and loss in a delay discounting task, was significantly correlated with impulsiveness and the Ann. N.Y. Acad. Sci (2013) C 2013 New York Academy of Sciences. 79

10 high-risk genotype for two GABRA SNPs. 129 Thus, the GABAergic system clearly plays a critical role in the initial stage of the addiction cycle. The withdrawal/negative affect stage The impaired affect regulation present in the withdrawal/negative affect stage involves a complex interplay of neurochemical and hormonal systems, including corticotropin-releasing hormone (CRH), cortisol, BDNF, neuropeptide Y, galanin, dynorphin, and substance P, along with various other neurotransmitter systems. 110 Several studies have investigated genetic variants that predispose individuals to experience differential degrees of withdrawal from addictive substances. For example, the dopamine transporter gene (DAT1) is related to severity of alcohol withdrawal, and AD patients carrying a particular risk variant in this gene were more likely to experience withdrawal seizures or delirium than patients lacking the risk allele. 130 Also, variation in the OPRM1 gene was found to modulate central dopaminergic sensitivity during acute withdrawal from alcohol. 131 Variation in the A118G polymorphism ofoprm1 determines dopamine binding in the VS in response to alcohol, such that individuals with the risky variant tend to have a more vigorous dopaminergic response to alcohol in this critical reward-related area. 132 The serotonin system is also a prime candidate for genetic studies of withdrawal, and a polymorphism in the serotonin transporter gene (5HTT) was significantly associated with severity of alcohol withdrawal symptoms. 133 Finally, a genetic linkage study found several loci possessing suggestive or significant linkage for DSM-IV nicotine withdrawal. 134 While investigators have clearly studied negative affect using neuroimaging, it has yet to be thoroughly explored from a genetic perspective. The preoccupation/anticipation stage Regarding the genetic underpinnings of the preoccupation/anticipation stage, evidence supports contributing roles for DRD4 and OPRM1 in modulating activity in neural structures involved in drinking motivation among heavy drinkers. 122 One SNP in the CRH-BP gene (rs ) moderates stressinduced alcohol craving, while the Asp40 allele in OPRM1 is related to cue-induced craving. 135 Further, variants in glycine and dopamine pathways predicted cue-induced cigarette craving, while variants in the CRH pathway were associated with stress-induced cigarette craving. 136 Finally, SNPs in DRD3 and SNCA were associated with alcohol 137, 138 craving. Combined neuroimaging and genetic research has examined adaptations in the preoccupation/anticipation stage. Our laboratory examined genetic variation in the tachykinin receptor 1 (TACR1) gene and neural responses to an alcohol craving task among individuals with AUDs. This study demonstrated that variation in 3 TACR1 SNPs predicted BOLD activation in response to alcohol cues in neural reward and reinforcement areas, consistent with previous work suggesting that a TACR1 antagonist influences fmri measures and relapse. 139 Additionally, our group found that the SNCA genotype was associated with the degree of fmri BOLD response in mesocorticolimbic areas during the alcohol taste exposure fmri paradigm. 140 A study of nicotine craving found that variation in CHRNA5, specifically rs , was associated with reactivity to smoking images in the hippocampus and DS among nicotine-dependent women. 141 Also, variation in CYP2A6, a gene involved in nicotine metabolism, was found to predict response to visual smoking cues in the amygdala, striatum, insula, and cingulate cortex. 142 The Go/No Go task has been used to examine the genetic basis of neural responses during response inhibition relevant to the preoccupation/anticipation stage. For example, polymorphisms in both DRD2 and DRD4 areassociated with differential activation during response inhibition on a Go/No Go task; in particular, activation differences are seen in the IFG (for the DRD4 VNTR polymorphism) and in the precuneus and cingulate gyrus (for the DRD2 polymorphism). 143 Finally, it is important to note that across the candidate gene studies using neuroimaging phenotypes discussed in this section, sample sizes ranged from 20 to 326 subjects, with nine studies including 50 or more subjects. While these studies may be small compared to GWAS or candidate gene studies, the typical phenotypes used in genetic studies are based on clinical interviews and diagnostic data. The sample sizes in such studies are indeed large compared to typical neuroimaging studies. However, the more important question is whether genetic studies that utilize neuroimaging phenotypes are adequately powered. There have been no systematic efforts at comparing power between genetic analyses of diagnostic phenotypes versus those that 80 Ann. N.Y. Acad. Sci (2013) C 2013 New York Academy of Sciences.

11 utilize neuroimaging phenotypes. However, studies have suggested that analyses using neuroimaging phenotypes are sufficiently powerful. 144 Additionally, as evidenced in this review, sample sizes for neuroimaging candidate gene studies tend to be larger than typical sample sizes used in neuroimaging research. Given the relatively small number of candidate studies that have been conducted using neuroimaging phenotypes, an encouraging degree of consistency across related phenotypes has been observed. Most notably, the DRD4 VNTR polymorphism has been linked to neural responses to alcohol cues 122 and smoking cues. 123 GABRA2 polymorphisms have been repeatedly found to be associated with AD and neural responses to alcohol cues Variation in OPRM1 has also consistently been shown to predict neural response to alcohol cues, 122, 132 and to be related to both alcohol craving 135 and withdrawal. 131 These findings are promising, but direct replication using identical phenotypes is needed in future studies, given the difficulties of replicating results in candidate gene studies more generally. Future studies: theory-driven integration of neuroimaging and genetic approaches Future studies seeking to integrate neuroimaging and genetics may benefit from a tight theoretical framework used to identify the most appropriate neuroimaging task and study design in a stagespecific manner. We provide a summary of fmri tasks that could be used effectively within each stage to identify genetic and epigenetic variation associated with adaptations in key networks (Table 1). For example, the binge/intoxication stage involves subtle changes in the balance between reward and control networks. An assessment of these neuronal adaptations may best be captured by tasks that utilize exposure to alcohol and drug cues to target the function of the control network, examine changes in the reward network, or in processes involved in the weighting of risk and reward (Table 1). To examine adaptations in this stage, it is also important to recruit samples of adolescents or young adults early in the substance use trajectory and to explore how these measures predict the future progression of alcohol and drug use. The most useful tasks for studying this stage would examine reward and risk taking, such as picture cue exposure tasks, 145 word cue exposure tasks, 49 BART 60 (used to examine decisions that involve risk and reward), delay discounting 62 (used to examine impulsive choices and immediate reward), monetary incentive delay 35, 59 (which targets reward, punishment, and incentive-driven behavior). These would best be used in adolescents and young adults in order to target early network adaptations. Phenotypes based on these neuroimaging tasks could be leveraged to identify genetic or epigenetic factors that moderate the trajectory of early substance-induced neuroadaptations. To target the neurobiological mechanisms of the withdrawal/negative affect stage, we propose drug cue-exposure tasks among individuals undergoing acute or protracted withdrawal compared to controls. This comparison may uncover unique neurobiological features present in individuals in the withdrawal stage. Additionally, emotional control tasks such as emotional face presentation could be used during withdrawal to determine the influence of withdrawal on emotion processing. 146 Similarly, stress exposure tasks utilizing individualized stress-scripts presented during varied withdrawal conditions may shed light on the neurobiological effects of stress on substance use and relapse (Table 1). 69 Finally, both cue and stress exposures may be used to examine the preoccupation/anticipation stage. The same cue and stress tasks as those used in the previous stages would apply to this stage when examining highly dependent populations and employing longitudinal experimental designs incorporating outcome data for linking neural network adaptations with compulsive drug taking and relapse. This stage may also be effectively targeted by drug anticipation tasks, 147 illuminating activation changes that occur in important brain areas in substance-dependent individuals expecting to receive their drug of choice. In addition, the Go/No Go paradigm would be effective for examining the substance-induced impulse control difficulties present among dependent users who are unable to discontinue substance use despite negative consequences Finally, the MSIT 85 can be applied to long-term substance users to examine differences in ability to respond to multiple cognitive demands. 85 There clearly exists some overlap between tasks targeting the three stages. However, as previously noted, the results of cue-exposure tasks among individuals in the later stages of addiction Ann. N.Y. Acad. Sci (2013) C 2013 New York Academy of Sciences. 81

12 Table 1. Suggested fmri tasks that could be used within each of the three stages of the addiction cycle to most effectively identify genetic and epigenetic markers associated with adaptations in key neural networks. The target population, potential study design, and proposed neural response phenotype targeted by each task are outlined in this table Concept Targeted/hypothesized targeted neural response Stage Population Task by task Design phenotype Binge/intoxication Adolescents/young adults Adolescents/young adults Adolescents/young adults Adolescents/young adults Adolescents/young adults Withdrawal/negative affect Middle to late adulthood; some symptoms of physiological or psychological dependence Middle to late adulthood; some symptoms of physiological or psychological dependence Middle to late adulthood; some symptoms of physiological or psychological dependence Balloon analog risk task (Lejuez et al. 57 ) Delay discounting (McClure et al. 58 ) Picture cue, word cue (Tapert et al., 145 Tapert et al. 49 ) Monetary incentive delay (Knutson et al. 59 ) Go/No Go (Bates et al., 30 Laurens et al., 32 Kiehl et al. 31 ) Taste cue (Myrick et al., 47 Filbey et al. 122 ) Emotional faces task (Hariri et al. 146 ) Personalized script task (Sinha et al. 68 ) Risk taking; balancing reward and loss Impulsivity; balancing small, immediate rewards vs. larger, delayed rewards Craving in response to visually rewarding stimuli Anticipation of receipt of monetary incentives Inhibitory ability; generating vs. withholding a response; suppressing incorrect tendency to respond Craving in response to a salient, rewarding, gustatory stimulus Differential responses to various emotional facial expressions Response to moderate levels of emotional distress Longitudinal design, predicts risk of future drinking Longitudinal design, predicts risk of future drinking Longitudinal design, predicts risk of future drinking Longitudinal design, predicts risk of future drinking Longitudinal design, predicts risk of future drinking Cross-sectional design, manipulates withdrawal condition Longitudinal design, administered during protracted withdrawal, predicts risk of future drinking; cross-sectional design, manipulates withdrawal condition Cross-sectional design, manipulates withdrawal condition Activation differences based on degree of risk-bias/risky decision making; dorsal ACC, anterior insula, striatum, OFC Differing degrees of network imbalance emphasizing impulsivity/low control/preference for immediate reward; OFC, IFG, dlpfc, SMA, insula, precuneus Subtle differences in activation related to incentive salience of drug-related cues; VTA, Nacc Changes or preexisting differences in reward and risk-taking bias; decreased VS activation with increased risk-bias Activation and performance differences in No Go targets following acute intoxication Depending on withdrawal condition, differential activation for drug compared to control cues; parietal, frontal, occipital, and central cortical regions, as well as DS and thalamus Disrupted activity of dlpfc, cingulate gyrus, OFC; differences in activation depending on withdrawal condition; CeA, BNST Depending on withdrawal condition, differential activation in areas related to control and emotion regulation,aswellasin craving-related areas such as DS Continued 82 Ann. N.Y. Acad. Sci (2013) C 2013 New York Academy of Sciences.