The Wiley Handbook on the Cognitive Neuroscience of Addiction

Stephen J. Wilson, Stephen J. Wilson

• Éditeur: Wiley-Blackwell
• Année: 2015
• ISBN:
  9781118472446
• Édition: 1
• Sujet: Psychologie
• Sous-sujet: Psychologie physiologique

This volume provides a thorough and up-to-date synthesis of the expansive and highly influential literature from the last 30 years by bringing together contributions from leading authorities in the field, with emphasis placed on the most commonly investigated drugs of abuse.

• Emphasises the most commonly investigated drugs of abuse, including alcohol, cocaine, nicotine, and opiates
• Brings together the work of the leading authorities in all major areas of the field
• Provides novel coverage of cutting-edge methods for using cognitive neuroscience to advance the treatment of addiction, including real-time neurofeedback and brain stimulation methods
• Includes new material on emerging themes and future directions in the use of cognitive neuroscience to advance addiction science

Section VI
Emerging Themes and Future Directions

18
Advancing Addiction Research through the Integration of Genetics and Neuroimaging

Hollis C. Karoly, Sarah L. Hagerty, Barbara J. Weiland, and Kent E. Hutchison

Introduction

Substance use disorders (SUDs) represent a major public-health burden in the United States, with lifetime prevalence rates estimated at 13–18% for alcohol abuse and roughly at 8% for drug abuse (Merikangas & McClair, 2012). Despite substantial individual and societal costs imposed by addictive disorders, current treatment approaches remain only modestly effective. In a recent meta-analysis of psychosocial treatments for illicit substance use, controlled-trial data demonstrated moderate effect sizes overall, treatment efficacy varying considerably across drugs of abuse (Dutra et al., 2008). Further, regarding licit substance use, results from the Combined Pharmacotherapies and Behavioral Interventions for Alcohol Dependence (COMBINE) study, one of the largest and most carefully controlled clinical trials, suggest that 80% of alcohol-dependent patients will have a relapse episode within 12 months after treatment (Anton et al., 2006).

In addition, according to a recent United Nations report, an estimated figure of 183,000 drug-related deaths was reported in 2012, and problem use has remained stable in recent years, impacting between 16 million and 39 million people worldwide. Alarmingly, there continues to be a gap in service provision such that only one in six problem drug users globally have had access to treatment or have received treatment for drug dependence each year (United Nations Office on Drugs and Crime, 2014). Thus the need for improved treatment options and greater treatment access is undeniable and grows increasingly urgent as rates of substance use continue to rise (US Department of Health and Human Services, 2011). Further, given the neurobiological complexity of addictive disorders (Volkow & Baler, 2014), the development of more efficacious treatments will undoubtedly require gaining a deeper mechanistic understanding of the characteristic neurobiological adaptations that occur during the progression from recreational to compulsive substance use.

In line with the goal formulated above, studies combining neuroimaging and genetics techniques may be especially illuminating. While the neuroscience of addiction has been studied in animal models for decades (Lynch, Nicholson, Dance, Morgan, & Foley, 2010; Weiss, 2010), neuroimaging has only recently allowed clinical neuroscientists to examine the same aspects of addiction in human studies (Morgenstern, Naqvi, DeBellis, & Breiter, 2013; Cadet, Bisagno, & Milroy, 2014). With respect to genetics, it appears that 50–60% of the variance in risk for SUDs is heritable (Schuckit, 2014), yet it has proven to be incredibly difficult to identify specific genetic variations that contribute to the genetic risk; and, when these variants are identified, it is difficult to translate changes at the molecular level into the neural adaptations that underlie the clinical expression of SUDs. The integration of neuroimaging and genetic approaches provides greater power for detecting genetic associations and for measuring the effects of those variations on neural phenotypes that may influence the etiology and/or the course of addictive disorders. Thus, combining neuroimaging and genetic approaches is particularly compelling as regards linking molecular genetic variation with observable neural phenotypes in order to ultimately characterize systemic and localized adaptations associated with prolonged substance use.

In this chapter we will review modern neurobiological conceptualizations of the etiology of addiction, as well as the neuroimaging studies that have informed these conceptualizations. In addition, we will review recent genetic studies and, most importantly, those studies that attempt to translate the effect of molecular genetic variation into neural mechanisms, emphasizing the benefits of combining these two powerful approaches. In particular, we will discuss this integrative work in the context of conceptual models of substance-induced alterations to neural reward and control processes. Finally, we will comment on the promise of epigenetic approaches and will offer suggestions for future research.

A Theoretical Framework for Addiction Research

In an effort to advance a practical, translational approach for the study of addiction neurobiology, we emphasize the importance of contextualizing findings within a broader conceptual framework that integrates molecular, neural, and clinical constructs. Fortunately numerous theoretical models of addiction processes have emerged over the last several decades. Grounded in both basic animal neuroscience and human cognitive neuroscience, such models have more recently converged on two primary brain networks that appear to strongly and directly influence substance use behavior. Namely, SUDs have been increasingly conceptualized in terms of neuroadaptations within reward and control pathways (Kalivas & Volkow, 2005; Hutchison, 2010; Koob & Volkow, 2010).

Specifically, substance use appears to be strongly influenced by the balance between the reward network (Kalivas & Volkow, 2005), which promotes the urge to use a substance, and the control network (Bechara, 2005), which determines whether an individual inhibits or acts upon such impulses (Hutchison, 2008). Briefly, the reward network includes brain structures related to pleasure and reinforcement – in particular, the ventral tegmental area, the nucleus accumbens (NAcc), the insula, and the amygdala (Koob & Le Moal, 2001; McFarland & Kalivas, 2001; Filbey, Schacht, Myers, Chavez, & Hutchison, 2009; Karoly, Harlaar, & Hutchison, 2013) – and the control network includes structures related to executive functioning – particularly the dorsolateral prefrontal cortex (DLPFC), the orbitofrontal cortex (OFC), and the inferior frontal gyrus (IFG) (Boettiger, Kelley, Mitchell, D’Esposito, & Fields, 2009; Claus, Kiehl, & Hutchison, 2011; Karoly et al., 2013).

Importantly, repeated substance use can lead to deleterious adaptations within both networks, such that – put simply – the reward network appears to be “strengthened” over time, while the control network becomes “weakened” (Koob & Le Moal, 2001; Baler & Volkow, 2006; Volkow et al., 2010; Weiland et al., forthcoming), thereby promoting continued substance use even despite negative consequences. Substance-induced epigenetic changes (e.g., DNA methylation) appear to influence adaptations within the reward network in particular (Maze & Nestler, 2011; Feng & Nestler, 2013; Nestler, 2014), while downstream consequences of substance-related neurotoxicity (i.e., neuroinflammation, particularly prolonged microglial activation) are thought to especially impact frontal control mechanisms (Liu et al., 2006; Qin & Crews, 2012; Mayfield, Ferguson, & Harris, 2013). Importantly, the characteristic imbalance between reward and control networks likely contributes to the often chronic, relapsing nature of SUDs (White, Boyle, & Loveland, 2002; Cornelius et al., 2003) and thus represents an important target for treatment research.

Not surprisingly, the balance of reward processing and inhibitory control appears to become increasingly dysregulated as the addiction cycle progresses from recreational to compulsive substance use (Karoly et al., 2013). We previously discussed the role of reward and control network adaptations within each stage of Koob’s 3-stage allostatic model of addiction (Karoly et al., 2013; Koob & Le Moal, 1997). Briefly, Koob’s model suggests that the addiction cycle progresses through three stages – binge/intoxication, withdrawal/negative affect, and preoccupation/anticipation – each of which is accompanied by adaptations that promote further substance use (Koob & Le Moal, 1997). Our “extended model” (Karoly et al., 2013) focuses on the shifting imbalance between reward and control networks across each stage of the addiction cycle, noting potential adaptations to reward and control networks that are likely to occur at each stage. For example, during the binge/intoxication stage, responses to drug-related rewards increase, while the incentive value of natural reinforcers tends to decrease (Robinson ...