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McLaughlin C, Fu QX, Na S, Heflin M, Chung D, Fiore VG, Gu X. Aberrant neural computation of social controllability in nicotine-dependent humans. Commun Biol 2024; 7:988. [PMID: 39143128 PMCID: PMC11324891 DOI: 10.1038/s42003-024-06638-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 07/26/2024] [Indexed: 08/16/2024] Open
Abstract
Social controllability, or the ability to exert control during social interactions, is crucial for optimal decision-making. Inability to do so might contribute to maladaptive behaviors such as smoking, which often takes place in social settings. Here, we examined social controllability in nicotine-dependent humans as they performed an fMRI task where they could influence the offers made by simulated partners. Computational modeling revealed that smokers under-estimated the influence of their actions and self-reported a reduced sense of control, compared to non-smokers. These findings were replicated in a large independent sample of participants recruited online. Neurally, smokers showed reduced tracking of forward projected choice values in the ventromedial prefrontal cortex, and impaired computation of social prediction errors in the midbrain. These results demonstrate that smokers were less accurate in estimating their personal influence when the social environment calls for control, providing a neurocomputational account for the social cognitive deficits in this population. Pre-registrations: OSF Registries|How interoceptive state interacts with value-based decision-making in addiction (fMRI study). OSF Registries|COVID-19: social cognition, mental health, and social distancing (online study).
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Affiliation(s)
- Caroline McLaughlin
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Qi Xiu Fu
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Soojung Na
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Matthew Heflin
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Dongil Chung
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea
| | - Vincenzo G Fiore
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Xiaosi Gu
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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2
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Kronberg G, Ceceli AO, Huang Y, Gaudreault PO, King SG, McClain N, Alia-Klein N, Goldstein RZ. Naturalistic drug cue reactivity in heroin use disorder: orbitofrontal synchronization as a marker of craving and recovery. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.11.02.23297937. [PMID: 37961156 PMCID: PMC10635268 DOI: 10.1101/2023.11.02.23297937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Movies captivate groups of individuals (the audience), especially if they contain themes of common motivational interest to the group. In drug addiction, a key mechanism is maladaptive motivational salience attribution whereby drug cues outcompete other reinforcers within the same environment or context. We predicted that while watching a drug-themed movie, where cues for drugs and other stimuli share a continuous narrative context, fMRI responses in individuals with heroin use disorder (iHUD) will preferentially synchronize during drug scenes. Results revealed such drug-biased synchronization in the orbitofrontal cortex (OFC), ventromedial and ventrolateral prefrontal cortex, and insula. After 15 weeks of inpatient treatment, there was a significant reduction in this drug-biased shared response in the OFC, which correlated with a concomitant reduction in dynamically-measured craving, suggesting synchronized OFC responses to a drug-themed movie as a neural marker of craving and recovery in iHUD.
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Affiliation(s)
- Greg Kronberg
- Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Ahmet O Ceceli
- Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Yuefeng Huang
- Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | | | - Sarah G King
- Icahn School of Medicine at Mount Sinai, New York, NY 10029
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3
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Pisupati S, Langdon A, Konova AB, Niv Y. The utility of a latent-cause framework for understanding addiction phenomena. ADDICTION NEUROSCIENCE 2024; 10:100143. [PMID: 38524664 PMCID: PMC10959497 DOI: 10.1016/j.addicn.2024.100143] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
Computational models of addiction often rely on a model-free reinforcement learning (RL) formulation, owing to the close associations between model-free RL, habitual behavior and the dopaminergic system. However, such formulations typically do not capture key recurrent features of addiction phenomena such as craving and relapse. Moreover, they cannot account for goal-directed aspects of addiction that necessitate contrasting, model-based formulations. Here we synthesize a growing body of evidence and propose that a latent-cause framework can help unify our understanding of several recurrent phenomena in addiction, by viewing them as the inferred return of previous, persistent "latent causes". We demonstrate that applying this framework to Pavlovian and instrumental settings can help account for defining features of craving and relapse such as outcome-specificity, generalization, and cyclical dynamics. Finally, we argue that this framework can bridge model-free and model-based formulations, and account for individual variability in phenomenology by accommodating the memories, beliefs, and goals of those living with addiction, motivating a centering of the individual, subjective experience of addiction and recovery.
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Affiliation(s)
- Sashank Pisupati
- Limbic Limited, London UK
- Princeton Neuroscience Institute & Department of Psychology, Princeton University, Princeton NJ, USA
| | - Angela Langdon
- National Institute of Mental Health & National Institute on Drug Abuse, National Institutes of Health, Bethesda MD, USA
| | - Anna B Konova
- Department of Psychiatry, University Behavioral Health Care & Brain Health Institute Rutgers University, New Brunswick NJ, USA
| | - Yael Niv
- Princeton Neuroscience Institute & Department of Psychology, Princeton University, Princeton NJ, USA
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4
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Gu X, McLaughlin C, Fu Q, Na S, Heflin M, Fiore V. Aberrant neural computation of social controllability in nicotine-dependent humans. RESEARCH SQUARE 2024:rs.3.rs-3854519. [PMID: 38343814 PMCID: PMC10854308 DOI: 10.21203/rs.3.rs-3854519/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Social controllability, defined as the ability to exert influence when interacting with others, is crucial for optimal decision-making. Inability to do so might contribute to maladaptive behaviors such as drug use, which often takes place in social settings. Here, we examined nicotine-dependent humans using fMRI, as they made choices that could influence the proposals from simulated partners. Computational modeling revealed that smokers under-estimated the influence of their actions and self-reported a reduced sense of control, compared to non-smokers. These findings were replicated in a large independent sample of participants recruited online. Neurally, smokers showed reduced tracking of forward projected choice values in the ventromedial prefrontal cortex, and impaired computation of social prediction errors in the midbrain. These results demonstrate that smokers were less accurate in estimating their personal influence when the social environment calls for control, providing a neurocomputational account for the social cognitive deficits in this population.
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Affiliation(s)
- Xiaosi Gu
- Icahn School of Medicine at Mount Sinai
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5
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Kato A, Shimomura K, Ognibene D, Parvaz MA, Berner LA, Morita K, Fiore VG. Computational models of behavioral addictions: State of the art and future directions. Addict Behav 2023; 140:107595. [PMID: 36621045 DOI: 10.1016/j.addbeh.2022.107595] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 11/23/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
Non-pharmacological behavioral addictions, such as pathological gambling, videogaming, social networking, or internet use, are becoming major public health concerns. It is not yet clear how behavioral addictions could share many major neurobiological and behavioral characteristics with substance use disorders, despite the absence of direct pharmacological influences. A deeper understanding of the neurocognitive mechanisms of addictive behavior is needed, and computational modeling could be one promising approach to explain intricately entwined cognitive and neural dynamics. This review describes computational models of addiction based on reinforcement learning algorithms, Bayesian inference, and biophysical neural simulations. We discuss whether computational frameworks originally conceived to explain maladaptive behavior in substance use disorders can be effectively extended to non-substance-related behavioral addictions. Moreover, we introduce recent studies on behavioral addictions that exemplify the possibility of such extension and propose future directions.
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Affiliation(s)
- Ayaka Kato
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Kanji Shimomura
- Physical and Health Education, Graduate School of Education, The University of Tokyo, Tokyo 113-0033, Japan
| | - Dimitri Ognibene
- Department of Psychology, Università degli Studi Milano-Bicocca, Milan, Italy; School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
| | - Muhammad A Parvaz
- Departments of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Laura A Berner
- Center of Excellence in Eating and Weight Disorders, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Center for Computational Psychiatry, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kenji Morita
- Physical and Health Education, Graduate School of Education, The University of Tokyo, Tokyo 113-0033, Japan; International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Tokyo 113-0033, Japan
| | - Vincenzo G Fiore
- Center for Computational Psychiatry, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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6
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Vinogradov S, Hamid AA, Redish AD. Etiopathogenic Models of Psychosis Spectrum Illnesses Must Resolve Four Key Features. Biol Psychiatry 2022; 92:514-522. [PMID: 35931575 PMCID: PMC9809152 DOI: 10.1016/j.biopsych.2022.06.024] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 06/22/2022] [Accepted: 06/22/2022] [Indexed: 01/07/2023]
Abstract
Etiopathogenic models for psychosis spectrum illnesses are converging on a number of key processes, such as the influence of specific genes on the synthesis of proteins important in synaptic functioning, alterations in how neurons respond to synaptic inputs and engage in synaptic pruning, and microcircuit dysfunction that leads to more global cortical information processing vulnerabilities. Disruptions in prefrontal operations then accumulate and propagate over time, interacting with environmental factors, developmental processes, and homeostatic mechanisms, eventually resulting in symptoms of psychosis and disability. However, there are 4 key features of psychosis spectrum illnesses that are of primary clinical relevance but have been difficult to assimilate into a single model and have thus far received little direct attention: 1) the bidirectionality of the causal influences for the emergence of psychosis, 2) the catastrophic clinical threshold seen in first episodes of psychosis and why it is irreversible in some individuals, 3) observed biotypes that are neurophysiologically distinct but clinically both convergent and divergent, and 4) a reconciliation of the role of striatal dopaminergic dysfunction with models of prefrontal cortical state instability. In this selective review, we briefly describe these 4 hallmark features and we argue that theoretically driven computational perspectives making use of both algorithmic and neurophysiologic models are needed to reduce this complexity and variability of psychosis spectrum illnesses in a principled manner.
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Affiliation(s)
- Sophia Vinogradov
- Department of Psychiatry & Behavioral Sciences, University of Minnesota Medical School, Minneapolis, Minnesota.
| | - Arif A Hamid
- Department of Neuroscience, University of Minnesota Medical School, Minneapolis, Minnesota
| | - A David Redish
- Department of Neuroscience, University of Minnesota Medical School, Minneapolis, Minnesota
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7
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A generative framework for the study of delusions. Schizophr Res 2022; 245:42-49. [PMID: 33648810 DOI: 10.1016/j.schres.2020.11.048] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 11/28/2020] [Accepted: 11/30/2020] [Indexed: 11/20/2022]
Abstract
Despite the ubiquity of delusional information processing in psychopathology and everyday life, formal characterizations of such inferences are lacking. In this article, we propose a generative framework that entails a computational mechanism which, when implemented in a virtual agent and given new information, generates belief updates (i.e., inferences about the hidden causes of the information) that resemble those seen in individuals with delusions. We introduce a particular form of Dirichlet process mixture model with a sampling-based Bayesian inference algorithm. This procedure, depending on the setting of a single parameter, preferentially generates highly precise (i.e. over-fitting) explanations, which are compartmentalized and thus can co-exist despite being inconsistent with each other. Especially in ambiguous situations, this can provide the seed for delusional ideation. Further, we show by simulation how the excessive generation of such over-precise explanations leads to new information being integrated in a way that does not lead to a revision of established beliefs. In all configurations, whether delusional or not, the inference generated by our algorithm corresponds to Bayesian inference. Furthermore, the algorithm is fully compatible with hierarchical predictive coding. By virtue of these properties, the proposed model provides a basis for the empirical study and a step toward the characterization of the aberrant inferential processes underlying delusions.
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8
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van den Ende MW, Epskamp S, Lees MH, van der Maas HL, Wiers RW, Sloot PM. A review of mathematical modeling of addiction regarding both (neuro-) psychological processes and the social contagion perspectives. Addict Behav 2022; 127:107201. [PMID: 34959078 DOI: 10.1016/j.addbeh.2021.107201] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 07/04/2021] [Accepted: 11/22/2021] [Indexed: 12/16/2022]
Abstract
Addiction is a complex biopsychosocial phenomenon, impacted by biological predispositions, psychological processes, and the social environment. Using mathematical and computational models that allow for surrogative reasoning may be a promising avenue for gaining a deeper understanding of this complex behavior. This paper reviews and classifies a selection of formal models of addiction focusing on the intra- and inter-individual dynamics, i.e., (neuro) psychological models and social models. We find that these modeling approaches to addiction are too disjoint and argue that in order to unravel the complexities of biopsychosocial processes of addiction, models should integrate intra- and inter-individual factors.
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9
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Moretta T, Buodo G. The Relationship Between Affective and Obsessive-Compulsive Symptoms in Internet Use Disorder. Front Psychol 2021; 12:700518. [PMID: 34456816 PMCID: PMC8387798 DOI: 10.3389/fpsyg.2021.700518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 07/20/2021] [Indexed: 11/20/2022] Open
Abstract
We investigated the relationships and diagnostic power of symptoms associated with affective disorders, obsessive-compulsive disorder, and drug addictions on Internet use disorder. Moreover, we tested whether Internet use disorder is characterized by a specific network of symptoms. One-hundred-and-four young adults (78 women) were assessed in laboratory using self-report measures of Internet addiction, alcohol use disorder, cannabis abuse, depression, anxiety, and stress symptoms, impulsiveness, and obsessive-compulsive symptoms. Only hoarding, obsessing, and depression symptoms were positively linked to Internet use disorder severity, with hoarding having greater power and accuracy than other obsessive-compulsive and affective symptoms. Only individuals with mild-moderate Internet use disorder were characterized by a network of strong and positive associations of affective and obsessive-compulsive symptoms. These findings may encourage future longitudinal studies aimed at identifying potential clinical criteria for the diagnosis of Internet use disorder and treatment targets.
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Affiliation(s)
- Tania Moretta
- Department of General Psychology, University of Padova, Padova, Italy
| | - Giulia Buodo
- Department of General Psychology, University of Padova, Padova, Italy
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10
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Mollick JA, Kober H. Computational models of drug use and addiction: A review. JOURNAL OF ABNORMAL PSYCHOLOGY 2020; 129:544-555. [PMID: 32757599 PMCID: PMC7416739 DOI: 10.1037/abn0000503] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In this brief review, we describe current computational models of drug-use and addiction that fall into 2 broad categories: mathematically based models that rely on computational theories, and brain-based models that link computations to brain areas or circuits. Across categories, many are models of learning and decision-making, which may be compromised in addiction. Several mathematical models take predictive coding approaches, focusing on Bayesian prediction error. Other models focus on learning processes and (traditional) prediction error. Brain-based models have incorporated prefrontal cortex, basal ganglia, and the dopamine system, based on the effects of drugs on dopamine, motivation, and executive control circuits. Several models specifically describe how behavioral control may transition from habitual to goal-directed systems, consistent with computational accounts of compromised "model-based" control. Some brain-based models have linked this to the transition of behavioral control from ventral to dorsal striatum. Overall, we propose that while computational models capture some aspects of addiction and have advanced our thinking, most have focused on the effects of drug use rather than addiction per se, most have not been tested on and/or supported by human data, and few capture multiple stages and symptoms of addiction. We conclude by suggesting a path forward for computational models of addiction. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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Affiliation(s)
- Jessica A Mollick
- Clinical and Affective Neuroscience Lab, Department of Psychiatry, Yale University
| | - Hedy Kober
- Clinical and Affective Neuroscience Lab, Department of Psychiatry, Yale University
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11
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Ognibene D, Fiore VG, Gu X. Addiction beyond pharmacological effects: The role of environment complexity and bounded rationality. Neural Netw 2019; 116:269-278. [PMID: 31125913 PMCID: PMC6581592 DOI: 10.1016/j.neunet.2019.04.022] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 04/06/2019] [Accepted: 04/25/2019] [Indexed: 02/03/2023]
Abstract
Several decision-making vulnerabilities have been identified as underlying causes for addictive behaviours, or the repeated execution of stereotyped actions despite their adverse consequences. These vulnerabilities are mostly associated with brain alterations caused by the consumption of substances of abuse. However, addiction can also happen in the absence of a pharmacological component, such as seen in pathological gambling and videogaming. We use a new reinforcement learning model to highlight a previously neglected vulnerability that we suggest interacts with those already identified, whilst playing a prominent role in non-pharmacological forms of addiction. Specifically, we show that a dual-learning system (i.e. combining model-based and model-free) can be vulnerable to highly rewarding, but suboptimal actions, that are followed by a complex ramification of stochastic adverse effects. This phenomenon is caused by the overload of the capabilities of an agent, as time and cognitive resources required for exploration, deliberation, situation recognition, and habit formation, all increase as a function of the depth and richness of detail of an environment. Furthermore, the cognitive overload can be aggravated due to alterations (e.g. caused by stress) in the bounded rationality, i.e. the limited amount of resources available for the model-based component, in turn increasing the agent's chances to develop or maintain addictive behaviours. Our study demonstrates that, independent of drug consumption, addictive behaviours can arise in the interaction between the environmental complexity and the biologically finite resources available to explore and represent it.
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Affiliation(s)
- Dimitri Ognibene
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK; ETIC, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Vincenzo G Fiore
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Xiaosi Gu
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA; The Mental Illness Research, Education, and Clinical Center (MIRECC VISN 2) at the James J. Peter Veterans Affairs Medical Center, Bronx, NY, USA
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12
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Gu X, FitzGerald THB, Friston KJ. Modeling subjective belief states in computational psychiatry: interoceptive inference as a candidate framework. Psychopharmacology (Berl) 2019; 236:2405-2412. [PMID: 31230144 PMCID: PMC6697568 DOI: 10.1007/s00213-019-05300-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Accepted: 06/05/2019] [Indexed: 12/28/2022]
Abstract
The nascent field computational psychiatry has undergone exponential growth since its inception. To date, much of the published work has focused on choice behaviors, which are primarily modeled within a reinforcement learning framework. While this initial normative effort represents a milestone in psychiatry research, the reality is that many psychiatric disorders are defined by disturbances in subjective states (e.g., depression, anxiety) and associated beliefs (e.g., dysmorphophobia, paranoid ideation), which are not considered in normative models. In this paper, we present interoceptive inference as a candidate framework for modeling subjective-and associated belief-states in computational psychiatry. We first introduce the notion and significance of modeling subjective states in computational psychiatry. Next, we present the interoceptive inference framework, and in particular focus on the relationship between interoceptive inference (i.e., belief updating) and emotions. Lastly, we will use drug craving as an example of subjective states to demonstrate the feasibility of using interoceptive inference to model the psychopathology of subjective states.
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Affiliation(s)
- Xiaosi Gu
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, Box 1230, New York, NY, 10029, USA.
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, Box 1230, New York, NY, 10029, USA.
- Mental Illness Research, Education, and Clinical Center (MIRECC VISN 2) at the James J. Peter Veterans Affairs Medical Center, Bronx, NY, USA.
| | - Thomas H B FitzGerald
- School of Psychology, University of East Anglia, Norwich Research Park, Norwich, Norfolk, NR4 7TJ, UK
- Wellcome Centre for Human Neuroimaging, University College London, London, England
- Max Planck-UCL Centre for Computational Psychiatry and Ageing Research, Russell Square House, London, WC1B 5EH, UK
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, University College London, London, England
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13
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Sweis BM, Thomas MJ, Redish AD. Beyond simple tests of value: measuring addiction as a heterogeneous disease of computation-specific valuation processes. ACTA ACUST UNITED AC 2018; 25:501-512. [PMID: 30115772 PMCID: PMC6097760 DOI: 10.1101/lm.047795.118] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Accepted: 07/06/2018] [Indexed: 12/13/2022]
Abstract
Addiction is considered to be a neurobiological disorder of learning and memory because addiction is capable of producing lasting changes in the brain. Recovering addicts chronically struggle with making poor decisions that ultimately lead to relapse, suggesting a view of addiction also as a neurobiological disorder of decision-making information processing. How the brain makes decisions depends on how decision-making processes access information stored as memories in the brain. Advancements in circuit-dissection tools and recent theories in neuroeconomics suggest that neurally dissociable valuation processes access distinct memories differently, and thus are uniquely susceptible as the brain changes during addiction. If addiction is to be considered a neurobiological disorder of memory, and thus decision-making, the heterogeneity with which information is both stored and processed must be taken into account in addiction studies. Addiction etiology can vary widely from person to person. We propose that addiction is not a single disease, nor simply a disorder of learning and memory, but rather a collection of symptoms of heterogeneous neurobiological diseases of distinct circuit-computation-specific decision-making processes.
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Affiliation(s)
- Brian M Sweis
- Graduate Program in Neuroscience and Medical Scientist Training Program, University of Minnesota, Minneapolis, Minnesota 55455, USA.,Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - Mark J Thomas
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota 55455, USA.,Department of Psychology, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - A David Redish
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota 55455, USA
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14
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Walters CJ, Redish A. A Case Study in Computational Psychiatry. COMPUTATIONAL PSYCHIATRY 2018. [DOI: 10.1016/b978-0-12-809825-7.00008-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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15
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Friston KJ, Redish AD, Gordon JA. Computational Nosology and Precision Psychiatry. COMPUTATIONAL PSYCHIATRY (CAMBRIDGE, MASS.) 2017; 1:2-23. [PMID: 29400354 PMCID: PMC5774181 DOI: 10.1162/cpsy_a_00001] [Citation(s) in RCA: 76] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2016] [Accepted: 01/12/2017] [Indexed: 12/11/2022]
Abstract
This article provides an illustrative treatment of psychiatric morbidity that offers an alternative to the standard nosological model in psychiatry. It considers what would happen if we treated diagnostic categories not as causes of signs and symptoms, but as diagnostic consequences of psychopathology and pathophysiology. This reformulation (of the standard nosological model) opens the door to a more natural description of how patients present-and of their likely responses to therapeutic interventions. In brief, we describe a model that generates symptoms, signs, and diagnostic outcomes from latent psychopathological states. In turn, psychopathology is caused by pathophysiological processes that are perturbed by (etiological) causes such as predisposing factors, life events, and therapeutic interventions. The key advantages of this nosological formulation include (i) the formal integration of diagnostic (e.g., DSM) categories and latent psychopathological constructs (e.g., the dimensions of the Research Domain Criteria); (ii) the provision of a hypothesis or model space that accommodates formal, evidence-based hypothesis testing (using Bayesian model comparison); and (iii) the ability to predict therapeutic responses (using a posterior predictive density), as in precision medicine. These and other advantages are largely promissory at present: The purpose of this article is to show what might be possible, through the use of idealized simulations.
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Affiliation(s)
- Karl J. Friston
- The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, UCL, London WC1N 3BG, UK
| | - A. David Redish
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455
| | - Joshua A. Gordon
- Department of Psychiatry, Columbia University, New York, NY 10032
- Director: National Institute of Mental Health (NIMH), Bethesda MD 20814
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Redish AD, Schultheiss NW, Carter EC. The Computational Complexity of Valuation and Motivational Forces in Decision-Making Processes. Curr Top Behav Neurosci 2016; 27:313-33. [PMID: 25981912 PMCID: PMC4937458 DOI: 10.1007/7854_2015_375] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The concept of value is fundamental to most theories of motivation and decision making. However, value has to be measured experimentally. Different methods of measuring value produce incompatible valuation hierarchies. Taking the agent's perspective (rather than the experimenter's), we interpret the different valuation measurement methods as accessing different decision-making systems and show how these different systems depend on different information processing algorithms. This identifies the translation from these multiple decision-making systems into a single action taken by a given agent as one of the most important open questions in decision making today. We conclude by looking at how these different valuation measures accessing different decision-making systems can be used to understand and treat decision dysfunction such as in addiction.
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Affiliation(s)
- A David Redish
- Department of Neuroscience, University of Minnesota, Minneapolis, USA.
| | | | - Evan C Carter
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, USA
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Regier PS, Redish AD. Contingency Management and Deliberative Decision-Making Processes. Front Psychiatry 2015; 6:76. [PMID: 26082725 PMCID: PMC4450586 DOI: 10.3389/fpsyt.2015.00076] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2015] [Accepted: 05/07/2015] [Indexed: 11/26/2022] Open
Abstract
Contingency management is an effective treatment for drug addiction. The current explanation for its success is rooted in alternative reinforcement theory. We suggest that alternative reinforcement theory is inadequate to explain the success of contingency management and produce a model based on demand curves that show how little the monetary rewards offered in this treatment would affect drug use. Instead, we offer an explanation of its success based on the concept that it accesses deliberative decision-making processes. We suggest that contingency management is effective because it offers a concrete and immediate alternative to using drugs, which engages deliberative processes, improves the ability of those deliberative processes to attend to non-drug options, and offsets more automatic action-selection systems. This theory makes explicit predictions that can be tested, suggests which users will be most helped by contingency management, and suggests improvements in its implementation.
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Affiliation(s)
- Paul S. Regier
- Graduate Program in Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - A. David Redish
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
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18
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Abstract
Objective The role of planning in binge eating episodes is unknown. We investigated the characteristics of planning associated with food cues in binging patients. We studied planning based on backward reasoning, reasoning that determines a sequence of actions back to front from the final outcome. Method A cross-sectional study was conducted with 20 healthy participants, 20 bulimia nervosa (BN), 22 restrictive (ANR) and 23 binging anorexia nervosa (ANB), without any concomitant impulsive disorder. In neutral/relaxing, binge food and stressful conditions, backward reasoning was assessed with the Race game, promotion of delayed large rewards with an intertemporal discounting task, attention with the Simon task, and repeating a dominant behavior with the Go/No-go task. Results BN and to a lower extent ANB patients succeeded more at the Race game in food than in neutral condition. This difference discriminated binging from non-binging participants. Backward reasoning in the food condition was associated with lower approach behavior toward food in BN patients, and higher food avoidance in ANB patients. Enhanced backward reasoning in the food condition related to preferences for delayed large rewards in BN patients. In BN and ANB patients the enhanced success rate at the Race game in the food condition was associated with higher attention paid to binge food. Conclusion These findings introduce a novel process underlying binges: planning based on backward reasoning is associated with binges. It likely aims to reduce craving for binge foods and extend binge refractory period in BN patients, and avoid binging in ANB patients. Shifts between these goals might explain shifts between eating disorder subtypes.
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Heldmann M, Berding G, Voges J, Bogerts B, Galazky I, Müller U, Baillot G, Heinze HJ, Münte TF. Deep brain stimulation of nucleus accumbens region in alcoholism affects reward processing. PLoS One 2012; 7:e36572. [PMID: 22629317 PMCID: PMC3358316 DOI: 10.1371/journal.pone.0036572] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2010] [Accepted: 04/10/2012] [Indexed: 12/29/2022] Open
Abstract
The influence of bilateral deep brain stimulation (DBS) of the nucleus nucleus (NAcc) on the processing of reward in a gambling paradigm was investigated using H2[15O]-PET (positron emission tomography) in a 38-year-old man treated for severe alcohol addiction. Behavioral data analysis revealed a less risky, more careful choice behavior under active DBS compared to DBS switched off. PET showed win- and loss-related activations in the paracingulate cortex, temporal poles, precuneus and hippocampus under active DBS, brain areas that have been implicated in action monitoring and behavioral control. Except for the temporal pole these activations were not seen when DBS was deactivated. These findings suggest that DBS of the NAcc may act partially by improving behavioral control.
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Affiliation(s)
- Marcus Heldmann
- Department of Neurology, University of Magdeburg, Magdeburg, Germany.
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20
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Catanese J, Cerasti E, Zugaro M, Viggiano A, Wiener SI. Dynamics of decision-related activity in hippocampus. Hippocampus 2012; 22:1901-11. [PMID: 22535656 DOI: 10.1002/hipo.22025] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/12/2012] [Indexed: 11/07/2022]
Abstract
Place-selective activity in hippocampal neurons can be modulated by the trajectory that will be taken in the immediate future ("prospective coding"), information that could be useful in neural processes elaborating choices in route planning. To determine if and how hippocampal prospective neurons participate in decision making, we measured the time course of the evolution of prospective activity by recording place responses in rats performing a T-maze alternation task. After five or seven alternation trials, the routine was unpredictably interrupted by a photodetector-triggered visual cue as the rat crossed the middle of central arm, signaling it to suddenly change its intended choice. Comparison of the delays between light cue presentation and the onset of prospective activity for neurons with firing fields at various locations after the trigger point revealed a 420 ms processing delay. This surprisingly long delay indicates that prospective activity in the hippocampus appears much too late to generate planning or decision signals. This provides yet another example of a prominent brain activity that is unlikely to play a functional role in the cognitive function that it appears to represent (planning future trajectories). Nonetheless, the hippocampus may provide other contextual information to areas active at the earliest stages of selecting future paths, which would then return signals that help establish hippocampal prospective activity.
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Affiliation(s)
- Julien Catanese
- Collège de France, Laboratoire de Physiologie de la Perception et de l'Action, Paris, France
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Piray P, Keramati MM, Dezfouli A, Lucas C, Mokri A. Individual Differences in Nucleus Accumbens Dopamine Receptors Predict Development of Addiction-Like Behavior: A Computational Approach. Neural Comput 2010; 22:2334-68. [DOI: 10.1162/neco_a_00009] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Clinical and experimental observations show individual differences in the development of addiction. Increasing evidence supports the hypothesis that dopamine receptor availability in the nucleus accumbens (NAc) predisposes drug reinforcement. Here, modeling striatal-midbrain dopaminergic circuit, we propose a reinforcement learning model for addiction based on the actor-critic model of striatum. Modeling dopamine receptors in the NAc as modulators of learning rate for appetitive—but not aversive—stimuli in the critic—but not the actor—we define vulnerability to addiction as a relatively lower learning rate for the appetitive stimuli, compared to aversive stimuli, in the critic. We hypothesize that an imbalance in this learning parameter used by appetitive and aversive learning systems can result in addiction. We elucidate that the interaction between the degree of individual vulnerability and the duration of exposure to drug has two progressive consequences: deterioration of the imbalance and establishment of an abnormal habitual response in the actor. Using computational language, the proposed model describes how development of compulsive behavior can be a function of both degree of drug exposure and individual vulnerability. Moreover, the model describes how involvement of the dorsal striatum in addiction can be augmented progressively. The model also interprets other forms of addiction, such as obesity and pathological gambling, in a common mechanism with drug addiction. Finally, the model provides an answer for the question of why behavioral addictions are triggered in Parkinson's disease patients by D2 dopamine agonist treatments.
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Affiliation(s)
- Payam Piray
- Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
| | | | - Amir Dezfouli
- Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
| | - Caro Lucas
- Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
| | - Azarakhsh Mokri
- Department of Psychiatry, Roozbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran, and Department of Clinical Sciences, Iranian National Center for Addiction Studies, Tehran University of Medical Sciences, Tehran, Iran
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22
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Comer SD, Bickel WK, Yi R, de Wit H, Higgins ST, Wenger GR, Johanson CE, Kreek MJ. Human behavioral pharmacology, past, present, and future: symposium presented at the 50th annual meeting of the Behavioral Pharmacology Society. Behav Pharmacol 2010; 21:251-77. [PMID: 20664330 PMCID: PMC2913311 DOI: 10.1097/fbp.0b013e32833bb9f8] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
A symposium held at the 50th annual meeting of the Behavioral Pharmacology Society in May 2007 reviewed progress in the human behavioral pharmacology of drug abuse. Studies on drug self-administration in humans are reviewed that assessed reinforcing and subjective effects of drugs of abuse. The close parallels observed between studies in humans and laboratory animals using similar behavioral techniques have broadened our understanding of the complex nature of the pharmacological and behavioral factors controlling drug self-administration. The symposium also addressed the role that individual differences, such as sex, personality, and genotype play in determining the extent of self-administration of illicit drugs in human populations. Knowledge of how these factors influence human drug self-administration has helped validate similar differences observed in laboratory animals. In recognition that drug self-administration is but one of many choices available in the lives of humans, the symposium addressed the ways in which choice behavior can be studied in humans. These choice studies in human drug abusers have opened up new and exciting avenues of research in laboratory animals. Finally, the symposium reviewed behavioral pharmacology studies conducted in drug abuse treatment settings and the therapeutic benefits that have emerged from these studies.
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Affiliation(s)
- Sandra D Comer
- New York State Psychiatric Institute/Columbia University, 1051 Riverside Drive, NY 10032, USA.
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23
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Li CSR, Luo X, Sinha R, Rounsaville BJ, Carroll KM, Malison RT, Ding YS, Zhang S, Ide JS. Increased error-related thalamic activity during early compared to late cocaine abstinence. Drug Alcohol Depend 2010; 109:181-9. [PMID: 20163923 PMCID: PMC2875333 DOI: 10.1016/j.drugalcdep.2010.01.008] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2009] [Revised: 12/31/2009] [Accepted: 01/06/2010] [Indexed: 12/29/2022]
Abstract
Altered cognitive control is implicated in the shaping of cocaine dependence. One of the key component processes of cognitive control is error monitoring. Our previous imaging work highlighted greater activity in distinct cortical and subcortical regions including the dorsal anterior cingulate cortex (dACC), thalamus and insula when participants committed an error during the stop signal task (Li et al., 2008b). Importantly, dACC, thalamic and insular activity has been associated with drug craving. One hypothesis is that the intense interoceptive activity during craving prevents these cerebral structures from adequately registering error and/or monitoring performance. Alternatively, the dACC, thalamus and insula show abnormally heightened responses to performance errors, suggesting that excessive responses to salient stimuli such as drug cues could precipitate craving. The two hypotheses would each predict decreased and increased activity during stop error (SE) as compared to stop success (SS) trials in the SST. Here we showed that cocaine dependent patients (PCD) experienced greater subjective feeling of loss of control and cocaine craving during early (average of day 6) compared to late (average of day 18) abstinence. Furthermore, compared to PCD during late abstinence, PCD scanned during early abstinence showed increased thalamic as well as insular but not dACC responses to errors (SE>SS). These findings support the hypothesis that heightened thalamic reactivity to salient stimuli co-occur with cocaine craving and loss of self control.
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Affiliation(s)
- Chiang-shan R. Li
- Department of Psychiatry, Yale University, New Haven, CT 06519 USA,Department of Neurobiology, Yale University, New Haven, CT 06520 USA,Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520 USA,Address correspondence to: Dr. Chiang-shan Ray Li Connecticut Mental Health Center, S103 Department of Psychiatry, Yale University School of Medicine 34 Park Street New Haven, CT 06519 Phone: 203-974-7354 FAX: 203-974-7076
| | - Xi Luo
- Department of Psychiatry, Yale University, New Haven, CT 06519 USA,Department of Statistics, Yale University, New Haven, CT 06519 USA
| | - Rajita Sinha
- Department of Psychiatry, Yale University, New Haven, CT 06519 USA
| | - Bruce J. Rounsaville
- Department of Psychiatry, Yale University, New Haven, CT 06519 USA,VA Connecticut Healthcare System, West Haven, CT 06516 USA
| | - Kathleen M. Carroll
- Department of Psychiatry, Yale University, New Haven, CT 06519 USA,VA Connecticut Healthcare System, West Haven, CT 06516 USA
| | | | - Yu-Shin Ding
- Department of Diagnostic Radiology, Yale University, New Haven, CT 06519 USA,Positron Emission Tomography Center, Yale University, New Haven, CT 06519 USA
| | - Sheng Zhang
- Department of Psychiatry, Yale University, New Haven, CT 06519 USA
| | - Jaime S. Ide
- Department of Psychiatry, Yale University, New Haven, CT 06519 USA
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26
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van der Meer MAA, Redish AD. Covert Expectation-of-Reward in Rat Ventral Striatum at Decision Points. Front Integr Neurosci 2009; 3:1. [PMID: 19225578 PMCID: PMC2644619 DOI: 10.3389/neuro.07.001.2009] [Citation(s) in RCA: 105] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2008] [Accepted: 01/22/2009] [Indexed: 01/01/2023] Open
Abstract
Flexible decision-making strategies (such as planning) are a key component of adaptive behavior, yet their neural mechanisms have remained resistant to experimental analysis. Theories of planning require prediction and evaluation of potential future rewards, suggesting that reward signals may covertly appear at decision points. To test this idea, we recorded ensembles of ventral striatal neurons on a spatial decision task, in which hippocampal ensembles are known to represent future possibilities at decision points. We found representations of reward which were not only activated at actual reward delivery sites, but also at a high-cost choice point and before error correction. This expectation-of-reward signal at decision points was apparent at both the single cell and the ensemble level, and vanished with behavioral automation. We conclude that ventral striatal representations of reward are more dynamic than suggested by previous reports of reward- and cue-responsive cells, and may provide the necessary signal for evaluation of internally generated possibilities considered during flexible decision-making.
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27
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Redish AD, Jensen S, Johnson A. A unified framework for addiction: vulnerabilities in the decision process. Behav Brain Sci 2008; 31:415-37; discussion 437-87. [PMID: 18662461 PMCID: PMC3774323 DOI: 10.1017/s0140525x0800472x] [Citation(s) in RCA: 302] [Impact Index Per Article: 18.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
The understanding of decision-making systems has come together in recent years to form a unified theory of decision-making in the mammalian brain as arising from multiple, interacting systems (a planning system, a habit system, and a situation-recognition system). This unified decision-making system has multiple potential access points through which it can be driven to make maladaptive choices, particularly choices that entail seeking of certain drugs or behaviors. We identify 10 key vulnerabilities in the system: (1) moving away from homeostasis, (2) changing allostatic set points, (3) euphorigenic "reward-like" signals, (4) overvaluation in the planning system, (5) incorrect search of situation-action-outcome relationships, (6) misclassification of situations, (7) overvaluation in the habit system, (8) a mismatch in the balance of the two decision systems, (9) over-fast discounting processes, and (10) changed learning rates. These vulnerabilities provide a taxonomy of potential problems with decision-making systems. Although each vulnerability can drive an agent to return to the addictive choice, each vulnerability also implies a characteristic symptomology. Different drugs, different behaviors, and different individuals are likely to access different vulnerabilities. This has implications for an individual's susceptibility to addiction and the transition to addiction, for the potential for relapse, and for the potential for treatment.
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Affiliation(s)
- A. David Redish
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, , http://umn.edu/~redish/
| | - Steve Jensen
- Graduate Program in Computer Science, University of Minnesota, Minneapolis, MN 55455,
| | - Adam Johnson
- Graduate Program in Neuroscience and Center for Cognitive Sciences, University of Minnesota, Minneapolis, MN 55455,
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Abstract
AbstractIn our target article, we proposed that addiction could be envisioned as misperformance of a decision-making machinery described by two systems (deliberative and habit systems). Several commentators have argued that Pavlovian learning also produces actions. We agree and note that Pavlovian action-selection will provide several additional vulnerabilities. Several commentators have suggested that addiction arises from sociological parameters. We note in our response how sociological effects can change decision-making variables to provide additional vulnerabilities. Commentators generally have agreed that our theory provides a framework within which to site addiction and treatment, but additional work will be needed to determine whether our taxonomy will help identify and treat subpopulations within the addicted community.
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A framework for studying the neurobiology of value-based decision making. Nat Rev Neurosci 2008; 9:545-56. [PMID: 18545266 PMCID: PMC4332708 DOI: 10.1038/nrn2357] [Citation(s) in RCA: 1155] [Impact Index Per Article: 72.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Neuroeconomics is the study of the neurobiological and computational basis of value-based decision making. Its goal is to provide a biologically based account of human behaviour that can be applied in both the natural and the social sciences. This Review proposes a framework to investigate different aspects of the neurobiology of decision making. The framework allows us to bring together recent findings in the field, highlight some of the most important outstanding problems, define a common lexicon that bridges the different disciplines that inform neuroeconomics, and point the way to future applications.
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Münte TF, Heldmann M, Hinrichs H, Marco-Pallares J, Krämer UM, Sturm V, Heinze HJ. Nucleus Accumbens is Involved in Human Action Monitoring: Evidence from Invasive Electrophysiological Recordings. Front Hum Neurosci 2008; 1:11. [PMID: 18958225 PMCID: PMC2525987 DOI: 10.3389/neuro.09.011.2007] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2007] [Accepted: 01/03/2008] [Indexed: 11/13/2022] Open
Abstract
The Nucleus accumbens (Nacc) has been proposed to act as a limbic-motor interface. Here, using invasive intraoperative recordings in an awake patient suffering from obsessive-compulsive disease (OCD), we demonstrate that its activity is modulated by the quality of performance of the subject in a choice reaction time task designed to tap action monitoring processes. Action monitoring, that is, error detection and correction, is thought to be supported by a system involving the dopaminergic midbrain, the basal ganglia, and the medial prefrontal cortex. In surface electrophysiological recordings, action monitoring is indexed by an error-related negativity (ERN) appearing time-locked to the erroneous responses and emanating from the medial frontal cortex. In preoperative scalp recordings the patient's ERN was found to be significantly increased compared to a large (n = 83) normal sample, suggesting enhanced action monitoring processes. Intraoperatively, error-related modulations were obtained from the Nacc but not from a site 5 mm above. Importantly, cross-correlation analysis showed that error-related activity in the Nacc preceded surface activity by 40 ms. We propose that the Nacc is involved in action monitoring, possibly by using error signals from the dopaminergic midbrain to adjust the relative impact of limbic and prefrontal inputs on frontal control systems in order to optimize goal-directed behavior.
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Affiliation(s)
- Thomas F Münte
- Department of Neuropsychology, University of Magdeburg Magdeburg, Germany
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Johnson A, Redish AD. Neural ensembles in CA3 transiently encode paths forward of the animal at a decision point. J Neurosci 2007; 27:12176-89. [PMID: 17989284 PMCID: PMC6673267 DOI: 10.1523/jneurosci.3761-07.2007] [Citation(s) in RCA: 624] [Impact Index Per Article: 36.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2007] [Revised: 09/12/2007] [Accepted: 09/18/2007] [Indexed: 11/21/2022] Open
Abstract
Neural ensembles were recorded from the CA3 region of rats running on T-based decision tasks. Examination of neural representations of space at fast time scales revealed a transient but repeatable phenomenon as rats made a decision: the location reconstructed from the neural ensemble swept forward, first down one path and then the other. Estimated representations were coherent and preferentially swept ahead of the animal rather than behind the animal, implying it represented future possibilities rather than recently traveled paths. Similar phenomena occurred at other important decisions (such as in recovery from an error). Local field potentials from these sites contained pronounced theta and gamma frequencies, but no sharp wave frequencies. Forward-shifted spatial representations were influenced by task demands and experience. These data suggest that the hippocampus does not represent space as a passive computation, but rather that hippocampal spatial processing is an active process likely regulated by cognitive mechanisms.
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Affiliation(s)
| | - A. David Redish
- Department of Neuroscience, Univeristy of Minnesota, Minneapolis, Minnesota 55455
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