NEUROPSYCHOLOGY and, more recently neuroimaging, have added most valuable insights into the neurophysiological basis of drug addiction. This knowledge needs to be integrated into current models on drug use to achieve neuroscientists’ and clinicians’ goals to improve both early detection and the therapeutic process by developing more effective intervention tools. A joint effort of researchers and clinicians is needed to determine whether cerebral dysfunctions are pre-existing and therewith predisposing or an effect of cumulative drug use. However, identifying neuropsychological and new biological markers of susceptibility to drug addiction or drug relapse raise important ethical concerns, e.g. “what does it mean to be at risk for drug addiction?,” “should an individual be aware of its increased risk or could that add the pressure needed to put him over the edge to consume drugs?,” or “how should we approach an individual in treatment who is very likely to relapse?”
We can only begin to address these concerns if we have a clear status of the current scientific knowledge. The goal of this chapter is to outline the current status of drug addiction research as it relates to the susceptibility to drug addiction. The specific emphasis is put on the neuropsychological and neurofunctional characteristics of drug use and their relation to personality and other modulating variables.
Among 15–64-year-olds, the most recent World Drug Report describes annual prevalence rates of up to 17% for marijuana (Canada 17%, USA 12%, Italy and Spain 11%, UK 8%) and considerably lower rates for opiates and psychostimulants (amphetamines: Australia 3%, USA 1.6%, UK 1.3%; cocaine: USA and Spain 3%, UK 2.6%). Other studies have found that as many as 1 in 10 young Americans aged 16–25 years reported misuse of stimulants in their lifetime (Wu et al. 2007) and past-year prevalence of non-medical use of prescription stimulants, which has raised significant concern (Wilens et al. 2002), has been estimated at about 5% among those aged 18–25 years. Facing high cognitive demands in vulnerable ages, college students are known to misuse psychostimulants and were reported to have used amphetamines non-medically in the past year in 7% or in 8% in their lifetime (Teter et al. 2003). In addition, 5% reported past-year non-medical use of methylphenidate, and 3% reported past-year use of methamphetamine. In general, 17% of male and 11% of female US undergraduates reported lifetime non-medical prescription stimulant use (Hall et al. 2005). About 7 million people in the US consume marijuana at least weekly. In 2000, an estimated 76% of America’s 14.8 million illicit drug users (more than 11 million people) used marijuana either alone (59%) or in conjunction with other illicit drugs (17%), and about 7 million Americans used at least once weekly (US Department of Health and Human Services Substance Abuse and Mental Health Services Administration Office of Applied Studies 1999).
Substance use disorders (SUDs) are both phenotypically and genetically heterogeneous, which implies that different neurobiological mechanisms contribute to the development of the disease (Wong and Schumann 2008). For instance, there is a strong genetic association between parents with SUDs and the offspring’s risk for such disorders. Parental alcohol dependence and parental drug dependence are similarly associated with increased risks for nearly all offspring disorders, with offspring of alcohol and drug-dependent parents having approximately 2–3 times the odds for developing a disorder by late adolescence compared to low-risk offspring (Marmorstein et al. 2008). Exposure to parental smoking represents an environmental risk for substance use in adolescent offspring (Keyes et al. 2008). There appear to be sex-related differences in genetic versus environmental risk for substance use disorders. Specifically, whereas girls showed greater genetic risks, the risk for substance use disorders in boys was more determined by the shared environment, which typically reflects family dysfunction and deviant peers (Silberg et al. 2003).
On the other hand, children of parents with SUDs do not necessarily develop problem use and children without a genetic/familiar risk may show severe substance disorders. Recently, a number of externalizing or internalizing factors have been identified to be highly associated with substance use. The prevalence of attention deficit hyperactivity disorder (ADHD) for instance, is higher among individuals with SUDs (Arias et al. 2008), for review see Kalbag and Levin (2005), with estimated prevalences being up to three times higher than for the non-ADHD population. Rates of SUDs among ADHD subjects are as high as 40% (Kalbag and Levin 2005) as compared to the general public showing a ratio of about 15% and adults with a history of ADHD hold a doubled risk for developing SUDs compared to adults without ADHD (Biederman et al. 1998). Clear evidence for a causal relationship between ADHD and SUDs has not been stated. Among others, Kalbag and Levin (2005) proposed that ADHD and SUDs may be based on common genetic risk factors and similar personality factors and psychosocial environmental factors and that the co-occurrence of ADHD and SUDs may derive from self-medication of ADHD symptoms. Externalizing behavior, i.e. an increased frequency of aggressive behavior, delinquency, and hyperactivity, is well known to constitute a risk factor for alcohol-related problems (for review see Zucker 2008) and SUDs (for review see Kendler et al. 2003) for the transition from adolescent to adult substance use (Lansford et al. 2008) as well as for poor treatment outcome (Winters et al. 2008). Although the construct of externalizing behavior is comprised of a set of different facets ranging from dishonesty, aggression, to illegal activities (Krueger et al. 2007), to be summarized as conduct symptoms in modern diagnostic classification systems, the effect of these behaviors may be due to a number of independent factors, which also include behavioral disinhibition (McGue et al. 2006; Iacono et al. 2008). For boys and girls, hyperactivity/impulsivity predicted initiation of all types of substance use, nicotine dependence, and cannabis abuse/dependence even when controlling for conduct symptoms. By contrast, relationships between inattention and substance outcomes disappeared when hyperactivity/impulsivity and conduct symptoms were controlled for. A categorical diagnosis of ADHD significantly predicted tobacco and illicit drug use only and a diagnosis of conduct disorder between the ages of 11–14 years was a powerful predictor of substance disorders at age 18 (Elkins et al. 2007).
Negative emotionality, i.e. the tendency to exhibit a stress response, interpersonal alienation, and aggressive behavior (Harkness et al. 1995), has also been shown to be related to increased use of alcohol (McGue et al. 1997) and other drugs (Lilienfeld and Penna 2001; Conway et al. 2002). However, whereas some investigators have found an increase in negative emotionality prior to use of drugs (Elkins et al. 2004), others did not show an increase in this construct among high-risk individuals (Swendsen et al. 2002) or found that the relationship did not hold up after controlling for evidence of an independent mood disorder (Riehman et al. 2002). These discrepancies may be due to complex gene-environment interactions, which have recently been reported for negative emotionality (Krueger et al. 2008). In particular, exposure to stressful live events in the presence of high negative emotionality resulted in an increased risk for subsequent drug use (King and Chassin 2008). For example, depressed mood and negative emotionality has been associated with stimulant use (Poulin 2007) and women with higher levels of depressive symptoms were more likely to use methamphetamine during a subsequent follow-up period (Semple et al. 2007). Moreover, individuals abusing methamphetamine were found to also report higher self-ratings of depression and anxiety (London et al. 2004).
Sensation seeking, i.e. the tendency to seek novel, varied, complex, and intense sensations and experiences (Zuckerman 1990), is another construct that has consistently been associated with an increased likelihood of drug use (Galizio et al. 1983; Jaffe and Archer 1987). Furthermore, individual differences in impulsivity and related constructs are consistently identified as key factors in the initiation and later problematic use of substances. Some authors have recently attempted to reframe thinking on adolescent impulsivity to include the positive as well as negative aspects of impulsivity as it relates to drug addiction (Gullo and Dawe 2008).
High prevalences of these psychiatric syndromes and personality traits in substance users suggest either a causal or modulating relationship. These factors may contribute both to the initial seeking out of psychoactive drugs as well as the continued use or could, as proposed for ADHD, be two expressions of the same, underlying genetic disposition or physiological basis. For example, individuals who scored high on impulsive sensation seeking reported significantly greater subjective effects following an acute administration of d-amphetamine (Kelly et al. 2006) but did not differ on inhibitory performance or risk-taking. Some have argued that this is mediated by the different experience high sensation seeking individuals report when using stimulants (Hutchison et al. 1999). These findings may relate to genetic differences across individuals. Genetic variation of the norepinephrine transporter gene for instance was found to be associated with increases in positive mood and elation after acute administration of amphetamine (Dlugos et al. 2007).
Large longitudinal studies carried out from early adolescence through adulthood would enable researchers to chronologically describe the development of both substance initiation, use, and problem use on the one hand and manifestations of such associated externalizing and internalizing factors on the other hand. The knowledge about the sequential development, however, would not necessarily reflect a causal relationship as one joint and underlying pathomechanism may be expressed. Huge samples and appropriate statistical approaches will be needed to further elucidate this question which can be of major impact for treatment planning. Psychoeducation and cognitive behavioral strategies could, for example, be indicated in high-risk adolescent subjects to prevent the development of substance use disorders.
Key symptoms of drug addiction are compulsive intake and the intense impulse to use drugs at the expense of other behaviors that could be more advantageous in the short or long term. Substance users show deficits in all three related components: (1) the expectation component based on reward predictions, the (2) compulsive “drive” component which is a motivational state, and the (3) decision-making component which is based on the motivational properties of the stimulus and the relative importance given to the expectation of immediate reward over possible long-term losses. These deficits suggest a deterioration of executive functioning, with executive functioning referring to a group of superior cognitive abilities of organization and integration. Executive functions are a flexible system performing and coordinating higher-order cognitive processes including anticipating and establishing goals, designing plans and programs, manipulation of memories, switching tasks, selective attention, self-regulation and monitoring of tasks, executive execution, and feedback and are associated with prefrontal brain functioning. They can directly contribute to drug use; they can, for example, increase the probability of drug-seeking behaviors and the vulnerability to relapse, even after long periods of abstinence. For example, response inhibition deficits are related to difficulties in controlling attentional biases and impulsive responses to drug stimuli (Hester et al. 2006). Further, executive dysfunctions may interfere with psychosocial behavioral treatment (Baicy and London 2007) such as cognitive behavioral therapy, currently the most effective treatments for stimulant dependence. Aharononich and colleagues (Aharonovich et al. 2003, 2006), for instance, reported negative correlations between cognitive performance and number of weeks in cognitive behavioral treatment or program drop-out rate. Prediction models for treatment compliance in cocaine-dependent subjects taking performance on the Stroop test, a measure for interference processing, into consideration outperformed models focusing on mood only (Streeter et al. 2008).
The nature of these neuropsychological deficits in substance users point towards disruptions in frontal, temporal, parietal, and basal ganglia systems. However, there is also a marked heterogeneity within dependent or abusing subjects, which may be due to both drug-related factors such as drug use patterns, e.g. drug type, duration, dosage, and drugunrelated factors such as demography, education, psychiatric, or neurological comorbidities. Studies on neuropsychological functioning in cocaine users for instance present inconsistent results, either showing severe (Gillen et al. 1998) or mild deficits (Goldstein et al. 2004; Woicik et al. 2008) or a lack of neuropsychological impairments (Hoff et al. 1996). The majority of studies, however, reports higher-order cognitive impairments (e.g. inhibitory dysregulation), impairment on tasks involving executive control, visuoperception, psychomotor speed, verbal learning and memory (for review see Yucel et al. 2007) in cocaine users.
The problem of whether neuropsychological dysfunctions are pre-existing or predisposing to drug use or a consequence of cumulative drug use has not been fully solved yet. Experimentally challenging longitudinal studies would be needed to examine neuropsychological functioning through adolescence. We recently gave evidence for impaired verbal learning and memory (Reske et al. 2010a) and verbal fluency capacities (Reske et al. 2010b) in young occasional users of prescription stimulants and/or cocaine, thus in individuals that have not developed stimulant-related problems. These results point to potentially pre-existing cognitive dysfunctions which may have led to stimulant initiation in the first place. A few predictionbased studies, have been carried out in children at high and low risk of substance use and found that deficits in behavioral regulation (referred to as “neurobehavioral inhibition,” deriving from primarily prefrontal tests) at age 16 predicted SUDs at age 19 with a 85% accuracy (Tarter et al. 2003; Kirisci et al. 2004). While both executive functioning and hyperactivity have been linked to substance abuse, childhood executive functioning served as a more salient predictor of drug use in early adolescence (Aytaclar et al. 1999). A series of cross-sectional neuropsychological studies has shown that cognitive performance of cocaine users depends on the length of use (Ardila et al. 1991; Rosselli and Ardila 1996). Others (Bolla et al. 2002) describe a persistent dose-related association between increasing number of joints per week and greater neurocognitive impairments (Medina et al. 2007) and an influence of lifetime marijuana use on psychomotor speed, complex attention, verbal memory, and sequencing. Chronic cannabis abusers in the non-toxicated state have shown impaired performance on a variety of attention, memory and executive function tasks (for review see Yucel et al. 2007) with deficits being attributed to duration and frequency of cannabis use and performance on cognitive tasks deteriorating with increasing years of heavy frequent cannabis use (Messinis et al. 2006).
Taken together, although definite conclusions as to whether neuropsychological impairments contribute to drug use cannot be drawn at this stage, it seems likely that at least subtle impairments exist prior to and potentially promote drug initiation. The emergence of neuroimaging techniques are now able to identify subtle brain dysfunctions in early drug users even in absence of subjective or neuropsychological test performance impairments. Thus, neuroimaging tools may soon help researchers and clinicians to solve this chicken and egg question. In the end, this knowledge will lead to a better understanding of the physiological basis of substance use disorders and, with practical relevance for diagnosis and treatment, to more effective and sensitive early detection tools.
Drug addiction is characterized by several key characteristics: (1) chronicity; (2) compulsive, habitual use of a substance; (3) difficulty in desisting use despite recognizing the harmful consequences; and (4) high probability of relapse even if attempts at abstinence are successful (Koob and Kreek 2007). As pointed out by Rachlin, the irrationality of drug addiction can be defined as the inability to appropriately predict ones own future behavior and acting upon those predictions to maximize reinforcement in the long run (Rachlin 2007).
Several theoretical approaches have conceptualized drug addiction:
• Incentive sensitization The incentive sensitization model was first proposed by Robinson and Berridge in 1993 (Robinson and Berridge 1993). In its original formulation, it stated that drug-using individuals experience a sensitization or hypersensitivity to the incentive motivational effects of drugs and drug-associated stimuli, which has been paraphrased as increased “wanting” of drugs and drug-related stimuli (Robinson and Berridge 2008). Several animal studies have implicated subcortical systems and, in particular, the ventral striatum and pallidum in the sensitization of hedonic dysregulation (Pecina and Berridge 2005).
• Allostatic dysregulation The allostatic regulation model is an extension of the stress regulation model (McEwen 1998), which states that allostasis is an unstable state of stress in which the brain and hypothalamic, pituitary, adrenal axis are chronically dysregulated (Koob and Kreek 2007). This model, developed by Koob and Le Moal (1997), was originally based on the opponent process theory (Solomon 1980). Drugs have profound effects on the reward-stress circuit and impair the ability to maintain a homeostatic equilibrium. Thus, repeated use drives a transition of behavioral processes from an impulsive mode of action, which is driven by pleasure relief and gratification, i.e. maintained by positive reinforcement, towards a compulsive mode of action, which is driven by relief of anxiety or stress, i.e. maintained by negative reinforcement. As addiction develops, individuals engage a cue-induced reinstatement or craving circuit that maintains compulsive use and promotes relapse during attempts at abstinence (Le Moal and Koob 2007). These authors have proposed that the ventral striatum, amygdala, and top-down modulatory structures such as corticothalamic feedback loops play important roles in the maintenance of drug-taking behavior via negative reinforcement.
• Habit formation Robbins and Everitt (Robbins and Everitt 1999) have proposed a transition from goal-directed action to habitual behavior during the development of drug addiction. In particular, these authors hypothesize that the transition from voluntary drug-related behaviors, which are regulated by outcomes, to a habitual mode of drug-motivated responding reflects a transition from prefrontal cortical to striatal control over responding as well as a transition from ventral to more dorsal striatal subregions (Everitt and Robbins 2005). Moreover, this transition is driven by instrumental learning via action-outcome contingencies of the control over interoceptive and exteroceptive states associated with drugs. This model involves different regions of the striatum, the amygdala, as well as top-down cortical control systems.
• Top-down and bottom-up, multi-stage failure models Redish and colleagues (2008) recently proposed a series of decision-related vulnerabilities that break down in addiction, following a two-systems conceptualization of processing with a top-down cognitive planning system (presumed cortical) and an associative habit network (presumed striatal). Among the failure modes is an increased sensitivity to associations between stimuli and available choices. Several other important related models include that of Kalivas and colleagues, in which addiction is viewed as pathological assignment of the salience of drug stimuli and neural regulation of behavioral output in response to those stimuli (Kalivas and Volkow 2005). Bechara similarly has conceptualized addiction as an instability as well as a disrupted equilibrium between a top-down control system and a bottom-up impulsive system that results in an increased propensity to engage in short-term reward seeking behavior (Bechara 2005).
• Interoceptive models Several authors (Naqvi and Bechara 2008; Verdejo-Garcia and Bechara 2008) have integrated recent findings implicating the insular cortex in addiction and concluded that there are at least two types of dysfunction in addiction. First, a hyperactivity in the amygdala or impulsive system, which exaggerates the rewarding impact of available incentives; and second a hypoactivity in the prefrontal cortex or reflective system, which forecasts the long-term consequences of a given action. As a consequence, overwhelming interoceptive input may act to maintain drug-taking behavior. These authors stress the importance of the interoceptive cues associated with drug use or with conditioned stimuli that elicit specific interoceptive responses.
• Dual-process model Some investigators have proposed a dual-process model whereby early adolescent problem behavior is associated with an increased risk of adult psychopathology because both are indicators of a common inherited liability and because early adolescent problem behavior increases the likelihood an adolescent is exposed to highrisk environments (McGue and Iacono 2008). Iacono and colleagues have proposed that a common genetic liability to behavioral disinhibition underlies the co-occurrence of these externalizing attributes, which may be expressed as altered processing of cognitive control, impulsivity and sensitivity to reward. In addition, exposure to various environmental risks further amplifies the risk associated with the common liability, increasing the likelihood of addiction generally (Iacono et al. 2008). Maturation-related suboptimal executive capacities in conjunction with inefficient behavioral control and emotional dysregulation are supposed to increase the risk for substance abuse and a developmental model proposes that dysfunctions of the prefrontal cortex (PFC) may at least partially, if not predominately underlie the liability for substance use disorders (see, e.g. Ivanov et al. 2008; Goldstein and Volkow 2002). Altered PFC activation is also found in youth with positive family history for SUD (Fryer et al. 2007). Others have suggested that SUDs are consequences of imbalances between overactive “impulsive” amygdala systems, which signal pain or pleasure of immediate prospect, and weakened “reflective” prefrontal cortex system for signaling pain or pleasure of future prospect (Bechara 2005). In particular, drugs and/or conditioned stimuli associated with the availability of drug are thought to overwhelm the goal-driven cognitive resources important for exercising the willpower to resist drugs (Bechara 2005).
In summary, these models stress the importance of (1) transition from positive reinforcement or voluntary, reward-related behavior to negative reinforcement, habitual or even compulsive use; (2) delineate a circuitry that involves the striatum, amygdala, insula, and prefrontal cortex; (3) and emphasize that homeostatic nature of drug addiction and its relationship to stress-related processes.
Neuroimaging is useful in linking psychologically defined processes to implementation in specific neural substrates of substance users. The following sections describe recent achievements of neuroimaging for addiction. First, recent imaging findings on relevant neuropsychological constructs such as decision making and inhibition shall be discussed in light of their implications for the understanding of the underlying processing dysfunction. Moreover, we propose that the degree of dysfunction in substance users can be more comprehensively assessed using neuroimaging, as neuroscientists are already able to assemble a picture of the physiological basis of addiction with different imaging techniques adding valuable information on the molecular, structural, and functional level, vastly exceeding the acquisition of one behavioral or experience-based measure like reaction time or craving. Neuroimaging can already be used to objectively measure symptom severity and to predict relapse which, reversely, should impact treatment planning.
Decision making is among the central dysfunctional behaviors in drug dependent individuals (Monterosso et al. 2001; Bechara et al. 2002; Bechara and Damasio 2002; Paulus 2007). Decision making consists of the process of transforming options into actions according to the individual’s preference, which may result in experiencing an outcome that leads to a different psychological and physiological state of the decision-maker. Operationally, one can divide decision making into three stages that occur over time (Ernst and Paulus 2005): (1) the assessment and formation of preferences among possible options, (2) the selection and execution of an action (and inhibition of inappropriate actions), and (3) the experience or evaluation of an outcome. An important aspect during the first stage—assessment and formation of preferences among possible options—is to assign value or utility to each of its available options (Kahneman and Tversky 1984), which determines the preference structure within the decision-making situation. In particular, the brain must not only evaluate what is occurring now, but also what may or may not occur in the future (Montague et al. 2006). Examining the behavioral and brain processes underlying decision-making provides an experimental window to understand whether drugs of abuse exacerbate an individual’s pre-existing inefficiency to function adaptively in everyday life. In particular, decisions by methamphetamine-dependent individuals are more influenced by the immediately preceding choice (Paulus et al. 2002), show a more rigid stimulus-response relationship (Paulus et al. 2003), and methamphetamine users are less well able to adjust their decision making to short-term versus long-term gains (Gonzalez et al. 2007). Neuroimaging helped identifying associated brain mechanisms. Methamphetamine users, for instance, performed a two-choice prediction task and, relative to control participants and the control task (two-choice response task), failed to activate, or activated less, regions within the dorsolateral, orbitofrontal, and right inferior cortices (Paulus et al. 2002). In addition, stimulant use duration could be predicted by orbitofrontal cortex activation with subjects with a shorter duration of methamphetamine use activating more within this region than subjects with a longer use history. These results hint at a progression in brain dysfunction and implications for behavior, which was supported by a follow-up study (Paulus et al. 2003), where days of sobriety were correlated with more activation in the left medial frontal gyrus (see later for more detail).
Inhibition, the process that overrides and reverses the execution of predominant thoughts, actions, or emotions, involves monitoring and stopping a planned or already ongoing behavior and presents one of the highest evolved human cognitive functions. It is closely linked to error processing, for example, during failed inhibition and a deficient capacity to control and inhibit behavior, psychologically manifested as irritability, reactive aggression, impulsivity, and sensation seeking, has been associated with the liability for early age onset of substance use disorders. From a theoretical point of view, inhibitory control is understood to encompass cognitive, affective, and behavioral processes modulated by the prefrontal cortex (Ivanov et al. 2008), which, as a top-down process, interacts with subcortical and posteriorcortical regions. Besides being found in a broad spectrum of psychiatric disorders, impaired inhibitory control is prominent in drug abuse. The majority of reports indicates that acute (de Wit et al. 2002b; Fillmore et al. 2002, 2006; Garavan et al. 2008), recreational (Colzato et al. 2007), and chronic cocaine use (Fillmore and Rush 2002; Kaufman et al. 2003; Li et al. 2006) as well as chronic methamphetamine use (Monterosso et al. 2005) have a negative impact on motor inhibitory control tasks. Specifically, stimulant users require more time to inhibit responses to stop-signals and show a lower probability of inhibiting responses, while response execution performance, measured via reaction times to go stimuli, did not differ (Fillmore and Rush 2002; Colzato et al. 2007; Li et al. 2008). D-amphetamine on the other hand, can improve performance on the stop task (decreasing stop times but not affecting gotimes), particularly in slow stopping subjects (de Wit et al. 2000, 2002). Reviewing 41 brain imaging studies, Dom and colleagues (2005) documented consistent differential activation in prefrontal areas in subjects with SUD on tasks of cognitive inhibition, supporting the relevance of prefrontal structures in SUD. Along with prefrontal dysfunctions, inhibitory processing in chronic cocaine use has been associated with diminished activation of anterior cingulate (Kaufman et al. 2003; Li et al. 2008) and insular regions (Kaufman et al. 2003), where measures of self-reported impulse control and emotion regulation were inversely correlated to anterior cingulate (ACC) blood oxygenation level-dependent (BOLD) signal (Li et al. 2008). Leland and colleagues (Leland et al. 2008) could show that abstinent methamphetamine dependent subjects activate the ACC to cues predicting the need to inhibit responses and that subsequent inhibition success increased with ACC activation.
Neuroimaging has been used recently to add an objective component to evaluations of symptom severity which previously relied on patients’ self reports or diagnostic assessments, which, again, depend on patients’ adequate descriptions. Substance abuse and dependence are often considered an all-or-none condition which is either present or absent with dependence and abuse assumed to arise once an individual reaches a defined threshold. This categorical model which is implemented in current classification systems contradicts clinical reality, where basic elements of the dependence process may be prominent in early stages of substance use but may quantitatively change during the development of abuse or dependence. Substance use disorders are not sufficiently quantified by presence or absence of abuse or dependence (Neale et al. 2006). Others proposed a severity index of drug use, which takes into account the number of uses and duration of use (Verdejo-Garcia et al. 2004), which was shown to be negatively correlated with performance on some executive functioning measures. For example, the severity of cocaine use was inversely related to performance on the Stroop-interference test (Verdejo-Garcia et al. 2005) and predicted greater disinhibition behavior (Verdejo-Garcia et al. 2006). A series of neuropsychological studies acknowledged this matter of debate and has shown that cognitive performance of cocaine users is related to the amount of recent use (Bolla et al. 2000), the duration of use (Ardila et al. 1991; Rosselli and Ardila 1996) as well as to the recency of use (Berry et al. 1993; Strickland et al. 1993). Some (Bolla et al. 2002) have reported a dose-related association between increasing number of joints per week and greater neurocognitive impairments. Recent brain imaging studies aimed at the neurofunctional characterization of symptom severity. Modeling nicotine dependence parametrically, Smolka and colleagues (2006), for instance, tested the hypothesis whether brain activation elicited by smoking cues increases with severity of nicotine dependence and report significant associations of dependence measures and ACC, parahippocampal, and parietal activation. ACC activation was also reported to be related to severity of nicotine dependence (McClernon et al. 2008). Hong et al. (2009) extended the focus beyond the ACC and showed that the severity of nicotine dependence is inversely correlated with connectivity between dorsal ACC and striatum. Even short-term nicotine challenge did not abolish this relation. Subjective measures such as experienced craving were also found to be correlated with neurofunctional measures (Bonson et al. 2002; Brody et al. 2002; Smolka et al. 2006).
A central characteristic of addictive behaviors is their chronically relapsing nature (Miller et al. 1996). However, “relapse” represents a somewhat arbitrary binary judgment imposed on a complex clinical condition. Moreover, the use of the term “relapse” has been criticized because it connotes an unrealistic and inaccurate conception of how successful change can occur over time (Miller 1996). Relapse is a complex process and includes multiple dimensions such as the process prior to re-use of the drug, the event of using the drug, the level to which the use returns, and the consequences associated with use (Donovan 1996). Several models that stress cognitive behavioral (Marlatt and Gordon 1985), person-situation interactional (Litman et al. 1984), cognitive appraisal (Sanchez-Craig 1976), and outcome expectation factors (Rollnick and Heather 1982; Annis 1991) have been put forth to explain the process of relapse. On the other hand, psychobiological models of relapse have been based on opponent process and acquired motivation theories (Solomon 1980), craving or loss of control (Ludwig et al. 1974), urges or craving (Wise 1988), withdrawal (Mossberg et al. 1985; Crosby et al. 1991), and kindling processes (Crosby et al. 1991). These models focus on the fact that brain reward systems become sensitized to drugs and drug-associated stimuli (Robinson and Berridge 2000) resulting in increased “drug-wanting,” which increases the susceptibility to relapse. Some investigators have suggested that relapse is best understood as having multiple and interactive determinants that vary in their temporal proximity from and their relative influence on relapse.
Several factors can predict outcomes: severity of dependence or withdrawal; psychiatric comorbidity; substance-related problems; motivation (abstinence commitment); length of treatment; negative affective states; cognitive factors; personality traits and disorders; coping skills; multiple substance abuse; contingency contracting or coercion; genetic factors; sleep architecture; urges and craving; self-efficacy; and economic and social factors (Ciraulo et al. 2003). Decision making, for example, has been proposed to represent an essential ingredient for understanding relapse (Allsop 1990). Performance on two tests of decision-making, but not on tests of planning, motor inhibition, reflection impulsivity, or delay discounting, was found to predict abstinence from illicit drugs at 3 months with high specificity and moderate sensitivity (Passetti et al. 2008). Surprisingly little research has been conducted to relate neurobiological variables to relapse susceptibility. In a longitudinal study of methamphetamine dependent individuals we used a two-choice prediction task to probe simple decision-related processing. By comparing brain activation during a two-choice response task relative to a two-choice prediction condition, we were able to separate sensorimotor processing from prediction and decision making. Individuals who engage in this task show bilateral activation of prefrontal cortex, striatum, posterior parietal cortex, and anterior insula during decision making (Paulus et al. 2001). Individuals who relapsed later on, but not those who did not, showed attenuated or reversed activation patterns in prefrontal, parietal, and insular cortical regions. Optimized prediction calculations based on step-wise discriminant function analyses revealed that right insula, right posterior cingulate, and right middle temporal gyrus response best differentiated between relapsing and non-relapsing methamphetamine-dependent subjects. In combination, we obtained 94% sensitivity, with 86% specificity using this approach. Using Cox regression analyses, we were also able to predict time to relapse. This study demonstrated that the attenuated activation patterns during decision making may play a critical role in processes that “set the stage” for relapse (Donovan 1996).
Drug cue-induced brain activation can also distinguish between subjects subsequently abstinent or relapsing (McClernon et al. 2007) and can be a better predictor of relapse than subjective reports of craving (Kosten et al. 2006). Focusing on striatum, ACC, and medial prefrontal cortex (mPFC), Grusser at al. (2004) showed that cue-induced brain activation was a better predictor of subsequent relapse in abstinent alcoholics than overt craving. Preliminary results specifically highlight roles for the medial prefrontal and sensory cortex, the anterior and posterior cingulate gyri, the insula, and the right middle temporal gyrus in relapse processes. Other studies combined initial brain scans with psychopathological and neuropsychological re-assessments in abstinent stimulant (Paulus et al. 2005) and cocaine (Sinha et al. 2005; Kosten et al. 2006) users, and a small sample of abstinent alcoholics (Grusser et al. 2004). These authors were also able to differentiate brain activation patterns of patients with subsequent drug use from those of abstinent patients.
The value of functional neuroimaging on its own compared to clinical, socio-demographical, and neuropsychological variables for the prediction of relapse in substance abuse has not been clearly delineated. A combination of data from all those modalities, however, may soon enable clinicians to deduce individual predictions, even though current imaging studies rely on group analyses. Further, other functional brain imaging techniques such as positron emission tomography (PET), single photon emission computed tomography (SPECT), and magneto encephalography (MEG) have already proven their relevance in predicting mild cognitive impairment (Matsuda 2007) and depression (Gemar et al. 2007) and may also be useful for the prediction of relapse to substance use.
Clinicians recognize that psychological interventions can profoundly alter patients’ sets of belief, ways of thinking, or behavior, but the putative mechanisms and underlying changes in the brain have only recently attracted the attention they deserve. Extending the initial use of functional magnetic resonance imaging (fMRI) to characterize the neural correlates of major psychiatric symptoms, researchers have now begun to develop study designs that incorporate fMRI into the treatment process directly. Indirectly or unintentionally, most substance treatments already employ paradigms that target brain areas crucial for addictive behavior. For example, prefrontal cortex functioning will be addressed by improvement of response selection by exploring advantages and disadvantages of continued drug use and the development of drug-refusal skills and coping mechanisms to overcome craving will improve dorsal ACC functioning. It has been reliably shown that training- and learningrelated changes in the brain can be detected with fMRI. So far, addiction medicine, however, has only rarely integrated brain imaging in such ways but researchers recognize this necessity and usefulness (Volkow et al. 2004; Etkin et al. 2005; Moras 2006; Yucel and Lubman 2007).
Kosten and colleagues (2006) presented videotapes showing cocaine smoking to recently abstinent cocaine dependent subjects while acquiring BOLD fMRI and assessing craving. Activation in sensory, posterior cingulate, and superior temporal cortical as well as lingual and occipital gyri showed significant correlations with treatment effectiveness. Subsequent relapsers and abstinent patients differed in their activation in right temporal and precentral, left occipital, and posterior cingulate cortical regions.
Applying a Stroop test during fMRI, Brewer et al. (2008) found self-reported longest durations of cocaine abstinence following treatment correlated with activation of the right putamen, left ventromedial prefrontal cortex extending into the orbitofrontal cortex and ventral ACC, as well as the posterior cingulate cortex. Inverse correlations between abstinence and activation emerged for the left dorsolateral prefrontal cortex (DLPFC).
Despite these obvious findings neuroimaging can add to the diagnostic and treatment processes in addiction medicine, resistance to the idea of integrating neuroscience and psychological/pharmacological treatment can be observed among practitioners and even among neuroscientists. Efficacy of treatment, though, is more likely to be based on brain functional differences than on how a patient is diagnosed and visionaries already picture cognitive emotional stress tests to predict treatment responsiveness (Etkin et al. 2005). One valuable application of brain imaging to aid in the diagnostic process may be its ability to differentiate among heterogeneous substance users according to distinct etiologies, which—in turn—could help to trigger more individualized clinical interventions. Thus, neuroimaging may in the end lead to the development of a more predictive severity measure of the disease. Neuroimaging may also one day help identifying the best treatment regime for individuals with substance use disorders, for instance cognitive behavioral strategies aiming at brain areas particularly deficient or hyperactive in a given patient. Results from neuroimaging studies are promising, but more research is needed to prove specificity and selectivity.
Substance use typically evolves in early to mid-adolescence, a neurodevelopmentally most vulnerable and crucial point in life. The temporal and causal association between substance use and brain development is still a matter of debate, but adolescent drug use clearly needs special consideration, both clinically and scientifically, to understand involved dysfunctions, to offer treatment at early stages, and to deduce to adult drug-use problems. We here give a brief overview about drug use in adolescence and its implications for adult use.
Alcohol use is consistently high among youth in the UK, with 91% of 15–16-year-olds reporting past year drinking (Hibell et al. 2004), and more significantly, 68% reported pastyear drunkenness. Thirty-eight per cent of 15–16-year-olds describe having tried cannabis at least once. Other drugs are used by 9% of UK adolescents, with most of the other drugs being inhalants (12%), and ecstasy (5%). The onset of substance use typically coincides with a critical maturation period of the human brain. Two key processes of brain maturation continue into adolescence and are particularly vulnerable to substance abuse: (1) synaptic refinement and (2) myelination. Synaptic refinement refers to the elimination of neural connectivity occurring after the ages of 9–11 years, when the peak number of synaptic connections is attained (Shaw et al. 2006). While overall brain size only changes slightly beyond early school-age (Giedd 2004), gray matter volume begins to decrease around puberty, largely due to synaptic refinement (Huttenlocher 1990) in subcortical and frontal regions which ends with the DLPFC (Gogtay et al. 2004). The DLPFC plays a key role in planning, organization, emotional regulation, response inhibition, and decision making, which, in return, are tightly connected to substance use. Volume increases in the hippocampus, a critical structure for encoding new information (Jernigan and Gamst 2005), and decreases in the thalamus (sensory integration) and nucleus accumbens (reward) also continue into young adulthood, concurrent with continuing myelination of the underlying white matter in this region (Jernigan and Gamst 2005). Myelination, the coating of axons of brain cells with myelin, results in the faster transmission of electrical signals along the axon shaft and causes an increase in size (or volume) of white brain matter until at least the second decade of life (Jernigan and Gamst 2005). White matter integrity in subcortical regions continues to improve until young adulthood (Snook et al. 2005).
Recent studies have shown that adolescence may be a period of heightened brain vulnerability for substance effects, such as alcohol (Spear and Varlinskaya 2005). Heavy drinking adolescents have been characterized with smaller volumes of the hippocampus (Medina et al. 2007) and prefrontal cortex (De Bellis et al. 2005) than age-matched non-drinkers. To date, no studies investigated effects of cannabis on neuromaturation. In reverse, however, adults who used cannabis before age 17 were shown to have smaller gray matter and larger white matter volumes than later-onset users (Wilson et al. 2000), suggesting abnormal progression of normal adolescent brain development. Taken together, these results emphasize the possibility that heavy drinking and early cannabis use significantly counteract adolescent neuromaturation. If substance abuse continues throughout adolescence, the brain may eventually not be able to compensate due to subtle neuronal damage, and task performance may begin to deteriorate. Female adolescents appear more vulnerable to deleterious effects of heavy drinking on brain function than males (Caldwell et al. 2005), suggesting an at least additional hormonal modulation.
Associated with effects on brain structural integrity, substances like alcohol, marijuana, and other drugs can have profound effects on neural substrates that are important for cognition and emotion. For example, adolescents exhibit distinct patterns of risk-taking behavior and evaluate rewards and punishments different from adults. Heavy alcohol use in adolescents is further associated with poorer scores on tests of information retrieval (Brown et al. 2000), attention (Tapert et al. 2002), and visuospatial functioning (Tapert et al. 2002) with impairments persisting even after extended periods of reduced or halted use (Brown et al. 2000; Tapert et al. 2002). Heavy nicotine use in adolescence has been associated with increased risk-taking propensity (Lejuez et al. 2003), and a longitudinal study (Tapert et al. 2002) of substance dependent youth suggested that more frequent adolescent stimulant use was associated with poorer attention, speeded psychomotor processing, and working memory in young adulthood. The fact that substance abuse strongly effects neuropsychological functioning is reflected by an inverse predictive association of cannabis use and follow-up attention functioning between the ages of 16–24 years (Tapert et al. 2002). However, some studies report no correlations between cannabis use and cognition (Teichner et al. 2000), and some abnormalities may predate use (Aytaclar et al. 1999), keeping the debate about chicken and egg open. Longitudinal studies are critical to elucidate the extent to which abnormalities pre-date the onset of regular substance use and long-term prospective studies of youth at high-risk are still urgently needed to one day enable researchers and clinicians to achieve an individual outcome prediction and therewith early-intervention possibilities.
Neuroimaging studies have shown that adolescents, compared to children and adults, differently engage neural systems, for instance, the nucleus accumbens when processing rewards and punishments. Children’s accumbens response to reward is linked to anticipating negative consequences while adults’ accumbens response is mainly associated with anticipating positive consequences. As opposed to both, adolescents’ nucleus accumbens activity relates to the anticipation of negative and positive consequences, and has been shown to be linked to the degree of risk-related behavior.
FMRI has also revealed that adolescent heavy drinkers show greatly enhanced response to alcohol cues in attention and reward-related brain regions (Tapert et al. 2003) and that abnormal brain response during spatial working memory (Tapert et al. 2004) in adolescent heavy drinkers as compared to non-drinkers exists despite intact task performance, suggesting reorganization and compensation for subtle neuronal injury. During nicotine withdrawal, cannabis-using adolescents showed poorer verbal recall, increased posterior activation, and disrupted frontoparietal connectivity than non-cannabis users, further implying that adolescent cannabis use may disrupt memory related substrates (Jacobsen et al. 2007). A long-lasting negative effect of cannabis has also been described in adolescent cannabis users where an increased brain processing effort during an inhibition task was present after a month of verified abstinence in dorsolateral prefrontal, parietal, and occipital regions (Tapert et al. 2007). Adolescent cannabis and nicotine users on the other hand performed less accurately on a working memory task than nicotine-only users and non-using controls, and showed greater fMRI response in the right hippocampus relative to other groups. This suggests that adolescent cannabis users might fail to inhibit hippocampal activity, perhaps due to changes in inhibitory neurotransmission (Jacobsen et al. 2004). Teenagers with comorbid alcohol and cannabis use disorders performed equally well but showed DLPFC responses increased over mono-substance users, which is consistent with the idea that these individuals need to expend an increased neural or computational effort to show appropriate levels of performance (Schweinsburg et al. 2005).
Taken together, neuropsychological and neuroimaging studies indicate that adolescent substance use is associated with neural disadvantages, particularly regarding executive functioning, attention, and learning. This will be based on negative effects on maturation of particularly frontal brain regions. On the other hand, pre-existing structural or functional brain abnormalities may themselves put adolescents at high risk for substance abuse. Longitudinal studies comparing substance naive youth with high-risk and problem users are necessary to better clarify the interaction between substance use, neuropsychological performance, and brain function.
As results from brain imaging gain more relevance for the diagnostic process, the planning of treatment, and for predictions of clinical outcomes, researchers and clinicians will need to better anticipate and evaluate implied ethical concerns. Neuroscientists will have to comment on the accuracy, validity, and reliability of functional neuroimaging. As long as those have not been proven, measures to guarantee confidentiality have to be implemented to protect individuals from premature conclusions.
Neuroscientists, clinicians, and ethicians will have to determine if, and how, to integrate neuroimaging results into diagnosis or the course of treatment. What, for instance, does it specifically mean to be at risk for drug addiction? Should an individual be made aware of its increased risk or could that add the pressure needed to put him/her over the edge to consume drugs? What shall be the next step after a subject at high risk has been identified? Should early intervention regimens be applied?
The predictive validity of functional imaging results has to be clearly demonstrated and the cost of imaging has to be weighed against its ability to generate better predictions. Recent results, however, clearly show that fMRI results outperform current prediction models. The medical and social consequences of predicting drug dependency or relapse, based on functional brain images, have to be acknowledged and effects on health insurance and stigmatization are obvious issues to be addressed by researchers and clinicians. It will have to be determined how we should approach an individual in treatment who is very likely to relapse. Moreover, should providers—as a consequence—suggest that a high/low-risk individual attend more or less intense treatment programs? In other areas of psychiatry early interventions are increasingly being considered. It appears that we are at a critical threshold and addiction medicine will need to discuss, identify, and in the end develop specific training and treatment tools for both diagnosis and relapse prevention.
Can imaging results be used directly, thus, can neuroscience help to overcome characterized cerebral dysfunctions? New applications such as neurofeedback using fMRI signal are appealing, but their transfer into patients’ lives outside the brain scanner have to be elaborated. On the other hand, cognitive behavioral therapy may develop trainings and treatments particularly addressing certain brain areas known to be deficient or overactive in drug users. While it seems unlikely, it cannot be ruled out that individuals at high risk or alreadyabusing subjects may be liable to increased stress of such neuroimaging testing. Along with results from neuroimaging investigations, it may add pressure or stress on the individual, potentially increasing risks involuntarily.
Lastly, neuroscientists along with clinicians and ethicians will need to discuss the legal responsibility and consequences of the identified neural basis of substance addiction. Is someone with substance addiction responsible for his/her actions? One popular philosophical view on drug use was originally formulated by Harry G. Frankfurt (1971) when elaborating his theory on the freedom of the will. Frankfurt discusses the concept of the free will, specifically drawing on examples of substance-dependent subjects. In his terms, a human, in contrast to most animals, has the peculiar characteristic of having “second-order desires.” Besides the wanting (e.g. consuming a drug) and choosing and being moved to do this or that, which would be subsumed under “first-order desires” in Frankfurt’s words, humans are also able to want to have (or not to have) certain desires and motives, thus, have the capacity for reflective self-evaluation. Individuals, however, who do have first-order desires but—independent of whether they have or not have second-order desires—have no second-order volitions (a person wants a certain desire to be his/her will), Frankfurt calls “wantons.” The distinction between a person and a wanton is explained illustrating “two narcotic addicts,” sharing the same physiological condition accounting for the addiction and both sharing a periodic desire for the drug. One of those dependent subjects hates his addiction and always struggles desperately, trying to overcome his addition. He has conflicting first-order desires as he wants to take the drug and to refrain from using it. In addition to these first-order desires, this dependent person has a second-order volition, namely to refrain from continuous use. The second, hypothetical user is a wanton. He uses the drug (according to his first-order desire) without being concerned whether those desires are desires by which he wants to be moved. He may have second-order desires, but he does not even reflect upon those. In Frankfurt’s terms, this substance user is “not different from an animal.” Further, is it supposed to be the wanton’s lack of capacity for reflection or his mindless indifference to the enterprise of evaluating his own desires and motives? While the unwilling dependent person’s will is not free (as the will to consume the drug is not the will he wants), this does not pose a problem to the wanton since he neither has the will he wants nor has the will that differs from the will he wants. He lacks the free will by default. Frankfurt, however, does not give an explanation as to why a substance user may be or become a person or a wanton. Philosophers and addiction medicine would need to cooperatively elaborate the practical, clinical, and legal relevance of this philosophical approach. Do certain (or all?) drugs affect second-order desires or volitions? Thus, does for instance cumulative use, whether initially wanted or not, lead to a deteriorating capacity for self-evaluation and reflection? Does continued use thus lead to dissolving a person’s free will? Are those wantons responsible for their actions? Neuroscientists, clinicians, ethicians, and may be lawyers will need to closely collaborate to solve pressing challenges deriving from recent neuroimaging findings, such as to which extent is it a substance dependent person responsible for his/her actions? If he is not, what are the consequences? Can neuroscientists and clinicians identify increased risks for deviant behaviors? Do neuroscientists and clinicians have the tools to prevent those or should the legal system be involved only? This dialogue should urgently be addressed and will be realizable if professional boundaries based on different theoretical constructs, communication strategies, and scientific backgrounds can be overcome.
Although functional brain imaging has become a powerful tool to investigate the physiological basis of neuropsychiatric disorders, it has only very recently been integrated into the diagnosis and treatment of SUDs. Since MRI instruments are widely available, the use new developments in magnetic resonance technology for identifying subjects at high risk, for treatment monitoring, and predicting individual differences in treatment responses and relapses is particularly appealing. In order for neuroimaging to play a critical clinical role for SUDs, its sensitivity and specificity must be documented. Thus far, most imaging studies have revealed intriguing results at the level of groups of subjects. To become clinically and therapeutically relevant, modern brain imaging has to be taken to the next step: How should results from diffusion tensor imaging, high-resolution structural brain imaging, or functional brain imaging of cognitive or emotional tasks be integrated into the individual treatment planning? In order for this modality to be useful for defining diagnostic categories or monitoring treatment success, we need to push the limits of this technology to clearly show its ability to define clinically-relevant information on a single-subject basis. Thus, it is not sufficient to show that ill individuals differ from healthy subjects but also that recovered or asymptomatic individuals with SUDs have altered processing in specific brain structures when compared to those individuals without SUDs. Such consistent distinctions can enable us to make clinical predictions about individuals who are at high risk for experiencing exacerbation of substance use symptoms. On the other hand, most imaging studies have demonstrated surprisingly large effect sizes, which supports the idea that differences across individuals and across time within individuals may be large enough to provide meaningful results when measured on a subject-by-subject basis.
Combining neuroimaging approaches within medications studies of substance users could prove useful for targeting specific pharmacological agents to subgroups of patients, prediction of response to medication, and relapse to use. Clearly, functional neuroimaging started playing an important role in substance use disorder research.
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