Reviews and meta-analysis
A Research Domain Criteria (RDoC) approach to Gambling Disorder: focus on preference-based decision-making and response inhibition
A. Marras1, N. Makris 2
1 Department of Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA), University of Florence, Florence, IT
2 Center for Morphometric Analysis, Departments of Psychiatry and Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Center for Morphometric Analysis, Departments of Psychiatry and Neurology,
Massachusetts General Hospital
Harvard Medical School, Boston, MA, USA
Gambling Disorder (GD) has been recently re-classified in the DSM-5 under the “substance-related and addictive disorders”, in light of its genetic, endophenotypic, and phenotypic resemblances to substance dependence. The clinical phenotype of this population is characterized by unsuccessful efforts to reduce or stop gambling despite negative outcomes, suggestive of aberrant decision-making mechanisms and faulty inhibitory control of gambling impulses that sustain the chronicity and comorbidities of this clinical syndrome. The main symptom clusters are represented by loss of control, craving/withdrawal, and neglect of other areas of life, whereas in a Research Domain Criteria (RDoC) perspective, GD patients exhibit deficits in the domain of “Positive valence systems”, particularly in the “Approach motivation” and “Reward learning” constructs, as well as in the “Cognitive systems”, primarily in the “Cognitive control” construct. In this paper we will focus on the symptomatic cluster of loss of control and we will review the main behavioral manifestations, task performances and corresponding putative neurocircuitries related to the RDoC framework.
Gambling disorder; neurocircuitry; research domain criteria; RDoC; domains
Gambling Disorder (GD) is an impulsive-compulsive disorder currently classified as an addictive disorder in the DSM-5 under the “substance-related and addictive disorders” . It is characterized by persistent and recurrent maladaptive patterns of gambling behavior, leading to impaired functioning . The inclusion of GD in the addictive disorder chapter of DSM-5 is motivated by the recognition of its genetic, endophenotypic, and phenotypic resemblances to substance dependence: both disorders show similar comorbidity patterns , genetic vulnerabilities, and responses to specific pharmacologic treatments .
The hallmark components of the disorder have been proposed to be (a) continued engagement in a behavior despite adverse consequences, (b) diminished self-control over engagement in the behavior, (c) compulsive engagement in the behavior, and (d) an appetitive urge or craving state prior to engaging in the behavior [4,5].
The diagnostic criteria for gambling disorder overlap largely with those for the substance use disorders: the main symptom clusters are represented by loss of control, craving/withdrawal, and neglect of other areas of life .
GD has 1-2% prevalence in the general population in Western societies [7,8], and is associated with substance misuses, depression, domestic violence, divorce, crime and suicide . The National Gambling Impact Study Commission estimated that the annual cost for GD is $5 billion (U.S.) per year and an additional $40 billion (U.S.) in lifetime costs for productivity reductions, social service, and creditor losses .
Herein, we will focus on the symptom cluster “loss of control” (i.e., unsuccessful efforts to control, cut back, or stop gambling), which appears to be related to deficits in executive functions (namely, diminished response inhibition ) and impaired reward-related decision-making . Behavioral manifestations, task performances and corresponding putative neurocircuitries will be reviewed in the context of the Research Domain Criteria (RDoC) framework.
Gambling disorder and the research domain criteria
The National Institute of Mental Health (NIMH) has recently launched the Research Domain Criteria (RDoC) project to overcome the limitations of current classification systems and to develop a framework for research on mental disorders that includes multiple dimensions : behavior, thought patterns, neurobiological measures, and genetics, with a strong focus on neurocircuitries. The RDoC aims at facilitating the incorporation of behavioral neuroscience in the study of psychopathology and at identifying reliable and valid psychological and biological mechanisms and their disruptions, with an eventual goal of understanding how abnormalities in these mechanisms drive psychiatric symptoms . RDoC’s strong focus on neural circuits is evident from the assumption that mental illnesses are conceptualized as brain disorders of brain circuits. Moreover, the RDoC assumes that dysfunctions in neural circuits can/will be identified by tools of neuroscience . Importantly, in the RDoC approach, the behavioral and genetic phenotypes are bridged and integrated through specific brain circuitries, which embody the level of systems biology [15-18].
Neurocircuitries are phenotypic targets of great potential for endophenotypic/biomarker discovery in current neuroimaging clinical research . In a RDoC perspective, GD patients exhibit deficits in the domain of “Positive valence systems”, particularly in the “Approach motivation” and “Reward learning” constructs, as well as in the “Cognitive systems”, more specifically in the “Cognitive control” construct [Figure 1].
Figure 1. RDoC domains involved in GD. A) Positive valence systems; B) Cognitive systems. Bold text indicates constructs and subconstructs involved in the symptom cluster “loss of control”. (Adapted from: NIMH RDoC Matrix https://www.nimh.nih.gov/research-priorities/rdoc/constructs/rdoc-matrix.shtml)
Positive valence systems are primarily responsible for responses to motivational situations such as reward seeking, consummatory behavior, and reward/habit learning . The construct of Approach motivation involves “mechanisms/processes that regulate the direction and maintenance of approach behavior influenced by pre-existing tendencies, learning, memory, stimulus characteristics, and deprivation states” (ibidem). Particularly relevant to GD is the subconstruct Reward valuation, which consists of “processes by which the probability and benefits of a prospective outcome are computed and calibrated by reference to external information, social context (e.g., group input, counterfactual comparisons), and/or prior experience. This calibration is influenced by pre-existing biases, learning, memory, stimulus characteristics, and deprivation states. Reward valuation may involve the assignment of incentive salience to stimuli” (ibidem).
Cognitive systems are responsible for various cognitive processes. Specifically, cognitive control “modulates the operation of other cognitive and emotional systems, in the service of goal-directed behavior, when prepotent modes of responding are not adequate to meet the demands of the current context. Additionally, control processes are engaged in the case of novel contexts, where appropriate responses need to be selected from among competing alternatives” .
Gambling Disorder domains: behavioral tasks and neurocircuitry
Positive Valence Systems
Approach motivation: preference-based decision-making
In a RDoC perspective, these processes involve an evaluation of costs/benefits and occurr in the context of multiple potential choices being available for decision-making .
Changes in reward based decision-making and increases in impulsivity are hallmark features of addiction  that has been scarcely studied satisfactorily in GD. Risky decision-making is a core feature of GD: gamblers have a high tolerance toward risk [23,24] and a bias to select short-term over long-term rewards is integral to the syndrome . This bias has been operationalized with the employ of a behavioral measure called delay discounting task  (DDT), in which participants choose between pairs of options that yield small, immediate vs. large, delayed rewards. Subjects with substance abuse and behavioral addictions show a tendency to choose small and immediate rewards rather than large and delayed rewards. The Iowa Gambling Task  (IGT) has also been employed as a measure of decision-making, since it is considered as the most widely used and ecologically valid measure of decision making in this clinical population. In the IGT, players are given four decks of cards and an endowment of fake money (e.g., $2000) and are instructed to select cards one at a time and try to lose the least amount of money and win the most. GD subjects have shown to perform worse on the IGT and to make more high-risk choices compared to controls, precisely after experiencing wins and losses . During high-risk gambling decisions, fMRI has shown that GD subjects exhibit relatively increased frontal lobe and basal ganglia activation, particularly involving the orbitofrontal cortex (OFC), caudate and amygdala. Increased activation of regions encompassing the extended reward pathway in GD subjects (GDs) during high risk choices suggests that the persistence of GD may be due to the increased salience of immediate and greater potential monetary rewards relative to lower monetary rewards or potential future losses (ibidem). There is also considerable evidence that GDs discount delayed rewards steeper than healthy controls . Neuroimaging research has shown that GD is associated with a shift in the interplay between a prefrontal-parietal control network and a brain network involved in immediate reward consumption , and a generally hypoactive reward system .
A differential activation of distinguishable neural systems between immediate and delayed choices has been highlighted, with the former driven by the limbic system (including the ventral striatum, medial orbitofrontal cortex (MOFC), medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC), and left posterior hippocampus) and the latter by the lateral prefrontal cortex and associated structures (including the right and left intraparietal cortex (RPar, LPar), right dorsolateral prefrontal cortex (DLPFC), right ventrolateral prefrontal cortex (VLPFC), and right lateral orbitofrontal cortex (LOFC)) .
More specifically, there is evidence that the right hemisphere plays an important role in inhibiting impulsive behavior and that the right DLPFC holds a certain role in the process of general decision-making . Although the pathophysiology of GD is not well understood, studies have shown altered brain activity in prefrontal regions (primarily the DLPFC) of GD patients in response to gambling stimuli. Recently, a hypersensitivity to extreme gain-loss ratios of dorsal cortico-striatal network involved in action–outcome contingencies has been shown in gamblers .
The similarity between GD and substance abuse has been repeatedly hypothesized on the basis of large overlaps between addictive manifestations of both disorders. Recently, an interesting contribution to a broader understanding of the neurocognitive features of GD, hypothesized a loss of willpower to resist gambling, deriving from a pathological usurpation of mechanisms of learning that under normal circumstances serve to shape survival behaviors related to the pursuit of rewards and the cues that predict them . This mechanism has been shown to be related with reward-based cognitive inflexibility, presumably resulting from an aberrant reward-based learning and observed as some kind of continuous gambling even in the face of increasing losses .
On a neurobiological perspective, reward-based cognitive inflexibility, has been associated with the orbitofrontal cortex (OFC – ), the ventral prefrontal cortex (vPFC – ), the ventrolateral prefrontal cortex (vl-PFC – ) and is facilitated by dopaminergic activity in the ventral regions of the striatum [37, 38].
Response inhibition refers the ability to suppress behaviors that are inappropriate, unsafe, or no longer required . Recent findings suggest that the ability to suppress automatic responses could be critical to gambling addictive behavior . Whereas the increased sensitization toward gambling-related cues appears to be related to a hyperactivity of impulsive processes that may explain gamblers’ motivation to seek out relevant reward , the unsuccessful efforts to reduce or stop gambling despite negative outcomes [41-43] are thought to depend on a dysregulation of the so-called “reflective system”, and specifically, a faulty inhibitory control, responsible for inadequate efforts to control (or cut back or stop) gambling (ibidem).
Inhibitory control has been usually assessed with behavioral measures such as the Stop Signal Task (SST ), in which subjects perform a choice reaction task, and, on a random selection of the trials, an auditory stop signal instructs subjects to withhold their response, or Go/No-Go tasks, which require people to make manual responses to rapidly presented visual or auditory cues (i.e., ‘Go’ stimuli), but to withhold responses in the presence of a different cue (‘No-Go’ stimuli) .
Deficits in behavioral and cognitive control constitute a symptom dimension associated with diminished response inhibition in experimental tasks. Impaired response inhibition performance (i.e. prolonged latency of motor response inhibition) has been previously highlighted in pathological gambling by using the stop-signal task and the go/no-go paradigm (for a review, see ) and recent contributions highlight the correlation between deficits in response inhibition and gambling severity [46-47].
Recent neuroimaging research suggests that response inhibition may depend on a fronto-basal-ganglia circuit, including the inferior frontal gyrus (IFG), the pre-supplementary motor area (pre-SMA) and the subthalamic nucleus (STN) and striatum . Both right IFG and pre-SMA
activation appear to be associated with successful stop trials. However, whereas right IFG contributes to response inhibition and not to monitoring performance or adjusting behavior, the pre-SMA seem to be involved in monitoring or resolving the conflict between the opposing task demands in the stop-signal paradigm. Also, fMRI studies showed inhibition-related activation in basal ganglia, including the STN and striatum and lesions to the basal ganglia impaired stop performance for both humans and rodents (ibidem).
Brain Circuitry underlying behavioral deficits in gambling disorder
The pre-SMA, which is located in the dorsomedial frontal cortex anterior to the leg representation of the primary motor cortex, has been suggested to be involved in cognitive control and impulsive choice reduction because of its role in updating or change of action plans, switching between tasks, and switching between rules linking stimuli to responses (see e.g., [49-62]). Moreover, the pre-SMA and the SMA both contain neurons encoding motivation to perform specific movements. The strength of this motivational signal reflects the amount of reward that is expected to follow from the action and, therefore, encodes an action value signal. These cortical centers are strongly innervated by the mesocorticolimbic system, including the ventral tegmental area (VTA), a dopaminergic system considered to be most at risk in addiction disorders, which is critical in mediating the hedonic impact of gambling and attributing incentive salience to reward-related gambling stimuli. Neurobiologically, dopamine deficiency seems to play a major role in pathological gambling and dorsal, ventral prefrontal cortex, SMA, pre-SMA as well as nucleus accumbens (NAc), Figure 2: Diagram of the dorsal and ventral prefrontal circuitry central to pre-SMA and SMA stimulation. Abbreviations: ACC: anterior cingulate cortex; BA: Brodmann’s area; FEF: frontal eye fields; IFG: inferior frontal gyrus; MFG: middle frontal gyrus; MPFC: medial prefrontal cortex; NAc: nucleus accumbens septi; OFC: orbitofrontal cortex; SMA: supplementary motor area; SN: substantia nigra; VTA: ventral tegmental area.
amygdala and hippocampus are connected and directly affected by the mesocorticolimbic network. Importantly, the gray matter centers in this network are interconnected via medium range and local frontal lobe connections and two principal long range fiber tracts, namely the cingulum bundle (CB) and the medial forebrain bundle (MFB). A simplified diagram of the dorsal and ventral prefrontal circuitry central to pre-SMA and SMA stimulation is shown in Figure 1.The anterior cingulate cortex (ACC, BA 24) is interconnected with the SMA (BA 6) and pre-SMA (BA 6) and higher-order association prefrontal cortices while at the same time receives inputs and innervates brainstem areas, the ventral striatum, the amygdala and hippocampal formation . Its unique structural connectivity enables the integration of autonomic, affective, cognitive and motor information making the prefrontal-congulate circuitry a major player in cognitive and motor control . Current structural and functional neuroimaging enables us to study the circuitry of the premotor areas in vivo (see e.g., [65-66]). A simplified diagram of the dorsal and ventral prefrontal circuitry central to pre-SMA and SMA stimulation is shown in Figure 2.
Figure 2: Diagram of the dorsal and ventral prefrontal circuitry central to pre-SMA and SMA stimulation. Abbreviations: ACC: anterior cingulate cortex; BA: Brodmann’s area; FEF: frontal eye fields; IFG: inferior frontal gyrus; MFG: middle frontal gyrus; MPFC: medial prefrontal cortex; NAc: nucleus accumbens septi; OFC: orbitofrontal cortex; SMA: supplementary motor area; SN: substantia nigra; VTA: ventral tegmental area.
The inclusion of GD in the “substance related and addictive disorders” chapter of DSM-5 recognizes the disorder as a prototypical behavioral addiction, characterized by symptom clusters of loss of control, craving/withdrawal, and neglect of other areas of life. Nevertheless, the paucity of neuroimaging, genetic and translational data on GD results in a still scarce understanding of the neurobiological underpinnings of the disorder. This would be crucial to the development of targeted medications, given that, despite its prevalence and impact, there are no medications with indications for treating GD and to date; no drug has an indication approval from the FDA . The adoption of a RDoC approach facilitates the identification of the neurobiological factors underlying the disorder by breaking up a complex psychiatric disorder into its components and domains and identifying the corresponding constructs and subconstructs, thus rendering the process more tangible and experimentally addressable. Importantly, RDoC constructs relate to biological and behavioral measures and may also help in identifying endophenotypes for the disorder. Therefore, recent research in GD is focusing on the identification of the neurobiological underpinnings of most employed behavioral tasks related to decision making and response inhibition (e.g. Iowa Gambling Task, Delayed Discounting Task, Stop Signal Task), to identify the neural correlates of the disorder’s symptomatologic clusters and domains. Herein, we focused on the symptom cluster “loss of control” (i.e., unsuccessful efforts to control, cut back, or stop gambling), which appears to be mainly related to impaired reward-related decision-making and deficits in executive functions. These deficits are associated with the RDoC domains of Positive Valence Systems (and its constructs of Approach motivation and Reward learning) and Cognitive Control (mainly its construct Response inhibition) respectively. Consistent with the RDoC matrix, deficits in preference-based decision-making have been identified in GD with the utilization of the IGT, revealing an involvement of numerous brain areas such as the striatum, amygdala, and OFC. Evidence regarding aberrant reward learning mechanisms are less robust, nevertheless they were hypothesized to be related with reward-based cognitive inflexibility and associated with an involvement of the OFC and ventral striatum, as highlighted in the RDoC matrix. Lastly, deficits in Cognitive control and particularly in the subconstruct of response inhibition have been identified in the disorder, using the SST and the Go/No-Go task, revealing the involvement of a fronto-striatal circuit and of the pre-supplementary motor area (pre-SMA). Further research is needed to expand our knowledge regarding the constructs of the disorder and how they correlate with the clinical presentation of the disorder as well as with the abnormalities at a neurocircuits level of explanation.
Conflict of Interest: The authors declare no conflict of interest.
Cite this article as
Marras, A. Makris, N. (2019). A Research Domain Criteria (RDoC) approach to Gambling Disorder: focus on preference-based decision-making and response inhibition. Archives of Behavioral Addictions, 1(1). doi: 10.30435/ABA.01.2019.06
- American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Arlington, VA, USA: American Psychiatric Publishing; 2013.1. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Arlington, VA, USA: American Psychiatric Publishing; 2013.
- Petry NM, Stinson FS, Grant BF: Comorbidity of DSM-IV pathological gambling and other psychiatric disorders: results from the National Epidemiologic Survey on Alcohol and Related Conditions. J Clin Psychiatry 2005, 66:564–574.
- Petry NM. Gambling and substance use disorders: current status and future directions. Am J Addict 2007, 16:1–9.
- Potenza MN. Should addictive disorders include non-substance-related conditions? Addiction. 2006; 101(1):142-51.
- Wareham JD, Potenza MN. Pathological gambling and substance use disorders. Am J Drug Alcohol Abuse. 2010; 36(5):242-7.
- Romanczuk-Seiferth N, van den Brink W, Goudriaan AE. From symptoms to neurobiology: pathological gambling in the light of the new classification in DSM-5. Neuropsychobiology. 2014;70(2):95–102.
- Welte JW, Barnes GM, Tidwell MC, Hoffman JH. The prevalence of problem gambling among U.S. adolescents and young adults: results from a national survey. J Gambl Stud 2008; 24, 119–133.
- Wardle, H., Moody, A., Spence, S., Orford, J., Volberg, R., Jotangia, D., et al., 2010. British gambling prevalence survey. National Centre for Social Research, London.
- Black DW, Shaw MC, McCormick BA, Allen J. Marital status, childhood maltreatment, and family dysfunction: a controlled study of pathological gambling. The Journal of clinical psychiatry. 2012;73(10):1293-7.
- Goodman R. The Luck Business: The Devastating Consequences and Broken Promises of America's Gambling Explosion. New York: Free Press; 1995.
- Goudriaan AE, Oosterlaan J, de Beurs E, van den Brink W. Neurocognitive functions in pathological gambling: a comparison with alcohol dependence, Tourette syndrome and normal controls. Addiction. 2006;101(4):534–47.
- Goudriaan AE, Oosterlaan J, de Beurs E, van den Brink W. Decision making in pathological gambling: a comparison between pathological gamblers, alcohol dependents, persons with Tourette syndrome, and normal controls. Cogn Brain Res 2005;23(1):137-151.
- Insel TR. The NIMH Research Domain Criteria (RDoC) Project: precision medicine for psychiatry. Am J Psychiatry. 2014;171(4):395-7.
- Sanislow CA, Pine DS, Quinn KJ, Kozak MJ, Garvey MA, Heinssen RK, Wang PS, Cuthbert BN. Developing constructs for psychopathology research: research domain criteria. J Abnorm Psychol. 2010 Nov;119(4):631-9.
- Breiter HC, Gasic GP, Makris N: Imaging the neural systems for motivated behavior and their dysfunction in neuropsychiatric illness; in Deiboeck TS, Kersh JY (eds): Complex Systems Science in Biomedicine. Heidelberg, Springer, 2006
- Hyman SE, Nestler EJ: The Molecular Foundations of Psychiatry. Washington, American Psychiatric Press, 1993
- Gottesman II, Gould TD: The endophenotype concept in psychiatry: etymology and strategic intentions. Am J Psychiatry 2003; 160:636–645
- Meyer-Lindenberg A, Weinberger DR: Intermediate phenotypes and genetic mechanisms of psychiatric disorders. Nat Rev Neurosci 2006;7:818–827.
- Makris N, Biederman J, Monuteaux MC, Seidman LJ. Towards conceptualizing a neural systems-based anatomy of attention-deficit/hyperactivity disorder. Dev Neurosci. 2009;31(1-2):36-49.
- National Institute of Mental Health (NIMH). Research Domain Criteria (RDoC). https://www.nimh.nih.gov/research-priorities/rdoc/constructs/positive-valence-systems.shtml (Retrieved on 01-2017)
- National Institute of Mental Health (NIMH). Research Domain Criteria (RDoC). https://www.nimh.nih.gov/research-priorities/rdoc/constructs/cognitive-systems.shtml.
- Bickel WK, Koffarnus MN, Moody L, Wilson AG. The behavioral and neuro-economic process of temporal discounting: a candidate behavioral marker of addiction. Neuropharmacology; 2014:76(Pt B):518–527.
- Clark L. Decision-making during gambling: an integration of cognitive and psychobiological approaches. Philos Trans R Soc Lond Ser B Biol Sci 2010; 365:319–330.
- Brevers D, Bechara A, Cleeremans A, Noel X. Iowa Gambling Task (IGT): twenty years after – gambling disorder and IGT. Front. Psychol. 2013; 4:665.
- Volkow ND, Baler RD. NOW vs LATER brain circuits: implications for obesity and addiction. Trends Neurosci 2015;38:345–52.
- Richards JB, Zhang L, Mitchell SH, de Wit H. Delay or probability discounting in a model of impulsive behavior: Effect of alcohol. J Exper Analysis Behav 1999;71(2):121-143.
- Bechara A, Damasio AR, Damasio H, Anderson SW. Insensitivity to future consequences following damage to human prefrontal cortex. Cognition. 1994; 50(1-3):7-15.
- Power Y, Goodyear B, Crockford D. Neural correlates of pathological gamblers preference for immediate rewards during the Iowa gambling task: an fMRI study. J Gambl Stud. 2012;28(4):623-36.
- Wiehler A, Peters J. Reward-based decision making in pathological gambling: the roles of risk and delay. Neurosci Res. 2015;90:3-14.
- Miedl SF, Wiswede D, Marco-Pallarés J, Ye Z, Fehr T, Herrmann M, Münte TF. The neural basis of impulsive discounting in pathological gamblers. Brain Imaging Behav. 2015;9(4):887-98.
- Miedl SF, Peters J, Büchel C. Altered neural reward representations in pathological gamblers revealed by delay and probability discounting. Arch Gen Psychiatry. 2012;69(2):177-86.
- McClure SM, Laibson DI, Loewenstein G, Cohen JD. Separate neural systems value immediate and delayed monetary rewards. Science. 2004;306(5695):503-7.
- Fleck MS, Daselaar SM, Dobbins IG, Cabeza R. Role of prefrontal and anterior cingulate regions in decision-making processes shared by memory and non memory tasks. Cereb Cortex 2006;6(11): 1623-1630
- Gelskov SV, Madsen KH, Ramsøy TZ, Siebner HR. Aberrant neural signatures of decision-making: Pathological gamblers display cortico-striatal hypersensitivity to extreme gambles. Neuroimage. 2016;128:342-52.
- Brevers D, Noël X. Pathological gambling and the loss of willpower: a neurocognitive perspective. Socioaffect Neurosci Psychol. 2013;3:21592.
- Boog M, Höppener P, Goudriaan AE, Boog MC, Franken IH. Cognitive inflexibility in gamblers is primarily present in reward-related decision making. Front Human Neurosci, 2014;8:569.
- Klanker M, Feenstra M, Denys D. Dopaminergic control of cognitive flexibility in humans and animals. Front Neurosci. 2013; 7:201.
- Clark L, Cools R, Robbins TW. The neuropsychology of ventral prefrontal cortex: decision-making and reversal learning. Brain Cogn. 2004; 55(1):41-53.
- de Ruiter MB, Veltman DJ, Goudriaan AE, Oosterlaan J, Sjoerds Z, van den Brink W. Response perseveration and ventral prefrontal sensitivity to reward and punishment in male problem gamblers and smokers. Neuropsychopharmacology. 2009; 34(4):1027-38.
- Chambers CD, Garavan H, Bellgrove MA. Insights into the neural basis of response inhibition from cognitive and clinical neuroscience. Neurosci Biobehav Rev. 2009;33(5):631-46.
- Makris N, Gasic GP, Seidman LJ, Goldstein JM, Gastfriend DR, Elman I, Albaugh MD, Hodge SM, Ziegler DA, Sheahan FS, Caviness VS Jr, Tsuang MT, Kennedy DN, Hyman SE, Rosen BR, Breiter HC. Decreased absolute amygdala volume in cocaine addicts. Neuron. 2004 Nov 18;44(4):729-40.
- Makris N, Gasic GP, Kennedy DN, Hodge SM, Kaiser JR, Lee MJ, Kim BW, Blood AJ, Evins AE, Seidman LJ, Iosifescu DV, Lee S, Baxter C, Perlis RH, Smoller JW, Fava M, Breiter HC. Cortical thickness abnormalities in cocaine addiction--a reflection of both drug use and a pre-existing disposition to drug abuse? Neuron. 2008 Oct 9;60(1):174-88.
- Makris N, Oscar-Berman M, Jaffin SK, Hodge SM, Kennedy DN, Caviness VS, Marinkovic K, Breiter HC, Gasic GP, Harris GJ. Decreased volume of the brain reward system in alcoholism. Biol Psychiatry. 2008 Aug 1;64(3):192-202.
- Logan GD, Cowan WB, Davis KA. On the ability to inhibit simple and choice reaction time responses: a model and a method. J Exp Psychol Hum Percept Perform. 1984;10(2):276-91.
- Stevens MC, Kiehl KA, Pearlson GD, Calhoun VD. Functional neural networks underlying response inhibition in adolescents and adults. Behav Brain Res. 2007;181(1):12-22.
- Brevers D, Cleeremans A, Verbruggen F, Bechara A, Kornreich C, Verbanck P, et al. Impulsive action but impulsive choice determines problem gambling severity. Plos One, 2012;7:e50647.
- Odlaug BL, Chamberlain SR, Kim SW, Schreibe L, Grant JE. A neurocognitive comparison of cognitive flexibility and response inhibition in gamblers with varying degrees of clinical severity. Psychol Med 2011;41:2111-19.
- Verbruggen F, Logan GD. Response inhibition in the stop-signal paradigm. Trends Cogn Sci. 2008; 12(11): 418–424.
- Matsuzaka Y, Aizawa H, Tanji J. A motor area rostral to the supplementary motor area (presupplementary motor area) in the monkey: neuronal activity during a learned motor task.J Neurophysiol. 1992 Sep;68(3):653-62.
- Matsuzaka Y, Tanji J. Changing directions of forthcoming arm movements: neuronal activity in the presupplementary and supplementary motor area of monkey cerebral cortex. J Neurophysiol. 1996 Oct;76(4):2327-42.
- Shima K, Mushiake H, Saito N, Tanji J. Role for cells in the presupplementary motor area in updating motor plans.Proc Natl Acad Sci U S A. 1996 Aug 6;93(16):8694-8.
- Picard N, Strick PL. Motor areas of the medial wall: a review of their location and functional activation.Cereb Cortex. 1996 May-Jun;6(3):342-53.
- Tanji J. Sequential organization of multiple movements: involvement of cortical motor areas. Annu Rev Neurosci. 2001;24:631-51.
- Schall JD, Stuphorn V, Brown JW. Monitoring and control of action by the frontal lobes.Neuron. 2002 Oct 10;36(2):309-22.
- Rushworth MF, Hadland KA, Paus T, Sipila PK. Role of the human medial frontal cortex in task switching: a combined fMRI and TMS study. J Neurophysiol. 2002 May;87(5):2577-92.
- Rushworth MF, Walton ME, Kennerley SW, Bannerman DM. Action sets and decisions in the medial frontal cortex.Trends Cogn Sci. 2004 Sep;8(9):410-7.
- Lau HC, Rogers RD, Haggard P, Passingham RE. Attention to intention. Science. 2004 Feb 20;303(5661):1208-10.
- Nachev P, Kennard C, Husain M.Functional role of the supplementary and pre-supplementary motor areas. Nat Rev Neurosci. 2008 Nov;9(11):856-69.
- Nachev P, Wydell H, O'neill K, Husain M, Kennard C. The role of the pre-supplementary motor area in the control of action. Neuroimage. 2007;36 Suppl 2:T155-63. Epub 2007 Mar 31.
- Nachev P, Rees G, Parton A, Kennard C, Husain M.Volition and conflict in human medial frontal cortex. Curr Biol. 2005 Jan 26;15(2):122-8.
- Ridderinkhof KR, van den Wildenberg WP, Segalowitz SJ, Carter CS. Neurocognitive mechanisms of cognitive control: the role of prefrontal cortex in action selection, response inhibition, performance monitoring, and reward-based learning. Brain Cogn. 2004 Nov;56(2):129-40.
- Akkal D, Dum RP, Strick PL. Supplementary motor area and presupplementary motor area: targets of basal ganglia and cerebellar output. J Neurosci. 2007 Oct 3;27(40):10659-73.
- Schmahmann J, Pandya D. (2006). Fiber pathways of the brain. OUP USA.
- Paus T. Primate anterior cingulate cortex: where motor control, drive and cognition interface. Nature Reviews Neuroscience 2001, 2(6), 417-424.
- Picard N, Strick PL. Imaging the premotor areas. Curr Opin Neurobiol. 2001 Dec;11(6):663-72.
- Klein JC, Behrens TE, Robson MD, Mackay CE, Higham DJ, Johansen-Berg H. Connectivity-based parcellation of human cortex using diffusion MRI: Establishing reproducibility, validity and observer independence in BA 44/45 and SMA/pre-SMA. Neuroimage. 2007 Jan 1;34(1):204-11.
- Bullock SA, Potenza MN. Pathological gambling: neuropsychopharmacology and treatment. Curr Psychopharmacol 2012; 1, 67–85Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, Sanislow C, Wang P. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry. 2010;167(7):748-51.