Abstract
Dual models of addictions, such as the impaired response inhibition and salience attribution (iRISA) model, propose that the breakdown of both appetitive and inhibitory processes underlie repeated drug seeking and taking in human drug addiction. More specifically, the iRISA model hypothesizes that the neural underpinnings of impairments in iRISA underlie the relapsing nature of addiction. In this chapter, we discuss the evidence for this model based on a recent, comprehensive, and systematic review of task-based neuroimaging studies in different addicted populations, including individuals with alcohol, cannabis, heroin, stimulant, and other addictions. The evidence from human neuroimaging studies supports a breakdown of six large-scale brain networks in addiction: the limbic-orbitofrontal reward network, the fronto-insular-parietal salience network, the prefrontal executive network, the fronto-parietal self-directed network, the subcortical habit, and memory networks. Taken together, the functional patterns observed in these networks provide evidence for (a) impairments in response inhibition and (b) a shift in salience attribution characterized by increased incentive valence/salience of drug cues, but decreased incentive valence/salience of monetary or social-emotional stimuli. Additionally, impaired learning may be a relevant subprocess in both iRISA deficits. Importantly, the observed abnormalities in brain activation levels were linked to clinical outcomes, such as risk for becoming addicted, escalation of drug use, and relapse after treatment. Overall, the reviewed data provide strong evidence for dual models of addiction.
Original language | English (US) |
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Title of host publication | Cognition and Addiction |
Subtitle of host publication | A Researcher’s Guide from Mechanisms Towards Interventions |
Editors | Antonio Verdejo García |
Place of Publication | London, England |
Publisher | Academic Press |
Chapter | 3 |
Pages | 17-23 |
Edition | 1st |
ISBN (Electronic) | 9780128152997 |
State | Published - 2020 |