2016
DOI: 10.1111/adb.12378
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Pathological gambling: a review of the neurobiological evidence relevant for its classification as an addictive disorder

Abstract: In light of the upcoming eleventh edition of the International Classification of Diseases (ICD-11), the question arises as to the most appropriate classification of “Pathological Gambling” (“PG”). Some academic opinion favors leaving “PG” in the “Impulse Control Disorder” (“ICD”) category, as in ICD-10, whereas others argue that new data especially from the neurobiological area favor allocating it to the category of “Substance-related and Addictive Disorders” (“SADs”), following the decision in the fifth revis… Show more

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Cited by 121 publications
(86 citation statements)
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References 97 publications
(98 reference statements)
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“…The results of the current study contribute to the existing evidence regarding shared neurocognitive and neurobiological processes between gambling and SUD . Similar to the presence of disturbances in reinforcement learning, reward processing and cognitive control, with similar changes in the neurocircuitry involved, gambling problems also involve the expression of endophenotypical markers of sensitized, implicit motivational processes towards gambling‐related cues , such as attentional and memory biases and, as of now, automatic approach tendencies.…”
Section: Resultssupporting
confidence: 57%
See 1 more Smart Citation
“…The results of the current study contribute to the existing evidence regarding shared neurocognitive and neurobiological processes between gambling and SUD . Similar to the presence of disturbances in reinforcement learning, reward processing and cognitive control, with similar changes in the neurocircuitry involved, gambling problems also involve the expression of endophenotypical markers of sensitized, implicit motivational processes towards gambling‐related cues , such as attentional and memory biases and, as of now, automatic approach tendencies.…”
Section: Resultssupporting
confidence: 57%
“…Gambling and substance use disorders (SUD) share many psychopathological features and brain and behavioural mechanisms , which played a role in the re‐classification of gambling disorder into the substance‐related and addictive disorders category in the DSM‐5 . Cognitive and neurobiological theories of addiction, such as dual‐process models and the incentive sensitization theory , may offer a framework for understanding why some individuals transition to problematic gambling, with serious negative consequences for their personal life and/or health.…”
Section: Introductionmentioning
confidence: 99%
“…Considering the importance of both reinforcement learning and addictive motivation in GD, both of these theories, or attempts to converge them (McClure et al, 2003;Zhang et al, 2009), may have important implications for gambling scholars (for a review see, Linnet, 2014). Both incorporate the activity of dopaminergic neurons in the ventral tegmental area that innervate the ventral striatum, and in turn, regions of the cortexsuch as the anterior cingulate cortex, orbitofrontal cortex, and insula cortexthat have been consistently implicated in reinforcement learning, and addictive behaviours, including gambling (Murch & Clark, 2016;Fauth-B€ uhler et al, 2017).…”
Section: Reinforcement Learning Addiction and The Brainmentioning
confidence: 99%
“…In the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders [DSM-5; American Psychiatric Association (APA), 2013], GD, previously named pathological gambling, has been recategorized from an impulse control disorder to an addictionrelated disorder. This change reflects the increasing awareness that the neurobiology of GD substantially overlaps with substance use disorders (SUDs; Leeman & Potenza, 2012;Fauth-B€ uhler et al, 2017). Both GD and SUDs are characterized by alterations and malfunction in reward, stress and cognitive-control circuits, which underlie manifestations such as compulsive gambling or compulsive drug consumption, craving, altered reward sensitivity, impaired selfcontrol and decision-making processes (Fontenelle et al, 2011;Robbins et al, 2012;Smith et al, 2014).…”
Section: Introductionmentioning
confidence: 99%