1
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Browning M, Carter CS, Chatham C, Den Ouden H, Gillan CM, Baker JT, Chekroud AM, Cools R, Dayan P, Gold J, Goldstein RZ, Hartley CA, Kepecs A, Lawson RP, Mourao-Miranda J, Phillips ML, Pizzagalli DA, Powers A, Rindskopf D, Roiser JP, Schmack K, Schiller D, Sebold M, Stephan KE, Frank MJ, Huys Q, Paulus M. Realizing the Clinical Potential of Computational Psychiatry: Report From the Banbury Center Meeting, February 2019. Biol Psychiatry 2020; 88:e5-e10. [PMID: 32113656 DOI: 10.1016/j.biopsych.2019.12.026] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 12/30/2019] [Accepted: 12/30/2019] [Indexed: 12/31/2022]
Affiliation(s)
- Michael Browning
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; Oxford Health National Health Service Foundation Trust, Warneford Hospital, Oxford, United Kingdom.
| | - Cameron S Carter
- Department of Psychiatry, University of California, Davis, Davis, California; Department of Psychology, University of California, Davis, Davis, California
| | - Christopher Chatham
- Department of Neuroscience and Rare Diseases, Roche Pharma Research and Early Development, Roche Innovation Center, Basel, Switzerland
| | - Hanneke Den Ouden
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Claire M Gillan
- School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Justin T Baker
- McLean Institute for Technology in Psychiatry, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | | | - Roshan Cools
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands; Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Peter Dayan
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - James Gold
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - Rita Z Goldstein
- Department of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York
| | | | - Adam Kepecs
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, New York
| | - Rebecca P Lawson
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - Janaina Mourao-Miranda
- Centre for Medical Image Computing, University College London, London, United Kingdom; Department of Computer Science, Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
| | - Mary L Phillips
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Diego A Pizzagalli
- Department of Psychiatry, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Albert Powers
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - David Rindskopf
- Educational Psychology, Graduate School and University Center of the City University of New York, New York, New York
| | - Jonathan P Roiser
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Katharina Schmack
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, New York
| | - Daniela Schiller
- Department of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Miriam Sebold
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Klaas Enno Stephan
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom; Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich, Zurich, Switzerland; Eidgenössische Technische Hochschule Zürich, Zurich, Switzerland; Max Planck Institute for Metabolism Research, Cologne, Germany
| | - Michael J Frank
- J. & Nancy D. Carney Institute for Brain Science, Department of Cognitive, Linguistic & Psychological Sciences, Brown University, Providence, Rhode Island
| | - Quentin Huys
- Department of Computer Science, Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom; Division of Psychiatry, University College London, London, United Kingdom; Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich, Zurich, Switzerland; Eidgenössische Technische Hochschule Zürich, Zurich, Switzerland
| | - Martin Paulus
- Laureate Institute for Brain Research, Tulsa, Oklahoma
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2
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Neuroimaging as a Tool for Individualized Treatment Choice in Depression: the Past, the Present and the Future. Curr Behav Neurosci Rep 2020. [DOI: 10.1007/s40473-020-00198-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Abstract
Purpose of Review
This paper aims to review the findings on neuroimaging as a tool for facilitating individualized treatment choice in depression.
Recent Findings
Neuroimaging has allowed the exploration of neural candidates for response biomarkers. In less than two decades, the field has expanded from small single drug studies to large multisite initiatives testing multiple interventions; from simple analytical methods to employing artificial intelligence, with an aim of establishing models based on a variety of data, such as neuroimaging, biological, psychological and clinical measures.
Summary
Neural biomarkers of response may play an important role in treatment response prediction. It seems likely that they will need to be considered together with other types of data in complex models in order to achieve the high accuracy and generalizability of results necessary for clinical use.
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3
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Feng C, Wang L, Li T, Xu P. Connectome-based individualized prediction of loneliness. Soc Cogn Affect Neurosci 2020; 14:353-365. [PMID: 30874805 PMCID: PMC6523423 DOI: 10.1093/scan/nsz020] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 02/12/2019] [Accepted: 03/12/2019] [Indexed: 12/20/2022] Open
Abstract
Loneliness is an increasingly prevalent condition linking with enhanced morbidity and premature mortality. Despite recent proposal on medicalization of loneliness, so far no effort has been made to establish a model capable of predicting loneliness at the individual level. Here, we applied a machine-learning approach to decode loneliness from whole-brain resting-state functional connectivity (RSFC). The relationship between whole-brain RSFC and loneliness was examined in a linear predictive model. The results revealed that individual loneliness could be predicted by within- and between-network connectivity of prefrontal, limbic and temporal systems, which are involved in cognitive control, emotional processing and social perceptions and communications, respectively. Key nodes that contributed to the prediction model comprised regions previously implicated in loneliness, including the dorsolateral prefrontal cortex, lateral orbital frontal cortex, ventromedial prefrontal cortex, caudate, amygdala and temporal regions. Our findings also demonstrated that both loneliness and associated neural substrates are modulated by levels of neuroticism and extraversion. The current data-driven approach provides the first evidence on the predictive brain features of loneliness based on organizations of intrinsic brain networks. Our work represents initial efforts in the direction of making individualized prediction of loneliness that could be useful for diagnosis, prognosis and treatment.
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Affiliation(s)
- Chunliang Feng
- Guangdong Provincial Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangzhou, China
| | - Li Wang
- Collaborative Innovation Center of Assessment toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Ting Li
- Collaborative Innovation Center of Assessment toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Pengfei Xu
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China.,Center for Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen, China.,Department of Neuroscience, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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4
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Zhukovsky P, Puaud M, Jupp B, Sala-Bayo J, Alsiö J, Xia J, Searle L, Morris Z, Sabir A, Giuliano C, Everitt BJ, Belin D, Robbins TW, Dalley JW. Withdrawal from escalated cocaine self-administration impairs reversal learning by disrupting the effects of negative feedback on reward exploitation: a behavioral and computational analysis. Neuropsychopharmacology 2019; 44:2163-2173. [PMID: 30952156 PMCID: PMC6895115 DOI: 10.1038/s41386-019-0381-0] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Revised: 03/16/2019] [Accepted: 03/25/2019] [Indexed: 11/11/2022]
Abstract
Addiction is regarded as a disorder of inflexible choice with behavior dominated by immediate positive rewards over longer-term negative outcomes. However, the psychological mechanisms underlying the effects of self-administered drugs on behavioral flexibility are not well understood. To investigate whether drug exposure causes asymmetric effects on positive and negative outcomes we used a reversal learning procedure to assess how reward contingencies are utilized to guide behavior in rats previously exposed to intravenous cocaine self-administration (SA). Twenty-four rats were screened for anxiety in an open field prior to acquisition of cocaine SA over six daily sessions with subsequent long-access cocaine SA for 7 days. Control rats (n = 24) were trained to lever-press for food under a yoked schedule of reinforcement. Higher rates of cocaine SA were predicted by increased anxiety and preceded impaired reversal learning, expressed by a decrease in lose-shift as opposed to win-stay probability. A model-free reinforcement learning algorithm revealed that rats with high, but not low cocaine escalation failed to exploit previous reward learning and were more likely to repeat the same response as the previous trial. Eight-day withdrawal from high cocaine escalation was associated, respectively, with increased and decreased dopamine receptor D2 (DRD2) and serotonin receptor 2C (HTR2C) expression in the ventral striatum compared with controls. Dopamine receptor D1 (DRD1) expression was also significantly reduced in the orbitofrontal cortex of high cocaine-escalating rats. These findings indicate that withdrawal from escalated cocaine SA disrupts how negative feedback is used to guide goal-directed behavior for natural reinforcers and that trait anxiety may be a latent variable underlying this interaction.
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Affiliation(s)
- Peter Zhukovsky
- Department of Psychology, University of Cambridge, Downing Street, Cambridge, CB2 3EB, UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
| | - Mickael Puaud
- Department of Psychology, University of Cambridge, Downing Street, Cambridge, CB2 3EB, UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
| | - Bianca Jupp
- Department of Psychology, University of Cambridge, Downing Street, Cambridge, CB2 3EB, UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
| | - Júlia Sala-Bayo
- Department of Psychology, University of Cambridge, Downing Street, Cambridge, CB2 3EB, UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
| | - Johan Alsiö
- Department of Psychology, University of Cambridge, Downing Street, Cambridge, CB2 3EB, UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
| | - Jing Xia
- Department of Psychology, University of Cambridge, Downing Street, Cambridge, CB2 3EB, UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
| | - Lydia Searle
- Department of Psychology, University of Cambridge, Downing Street, Cambridge, CB2 3EB, UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
| | - Zoe Morris
- Department of Psychology, University of Cambridge, Downing Street, Cambridge, CB2 3EB, UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
| | - Aryan Sabir
- Department of Psychology, University of Cambridge, Downing Street, Cambridge, CB2 3EB, UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
| | - Chiara Giuliano
- Department of Psychology, University of Cambridge, Downing Street, Cambridge, CB2 3EB, UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
| | - Barry J Everitt
- Department of Psychology, University of Cambridge, Downing Street, Cambridge, CB2 3EB, UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
| | - David Belin
- Department of Psychology, University of Cambridge, Downing Street, Cambridge, CB2 3EB, UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
| | - Trevor W Robbins
- Department of Psychology, University of Cambridge, Downing Street, Cambridge, CB2 3EB, UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
| | - Jeffrey W Dalley
- Department of Psychology, University of Cambridge, Downing Street, Cambridge, CB2 3EB, UK.
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK.
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 2QQ, UK.
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5
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Kaminski J, Katthagen T, Schlagenhauf F. [Computational psychiatry : Data-driven vs. mechanistic approaches]. DER NERVENARZT 2019; 90:1117-1124. [PMID: 31538209 DOI: 10.1007/s00115-019-00796-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
The emerging research field of so-called computational psychiatry attempts to contribute to an understanding of complex psychiatric phenomena by applying computational methods and to promote the translation of neuroscientific research results into clinical practice. This article presents this field of research using selected examples based on the distinction between data-driven and theory-driven approaches. Exemplary for a data-driven approach are studies to predict clinical outcome, for example, in persons with a high-risk state for psychosis or on the response to pharmacological treatment for depression. Theory-driven approaches attempt to describe the mechanisms of altered information processing as the cause of psychiatric symptoms at the behavioral and neuronal level. In computational models possible mechanisms can be described that may have produced the measured behavioral or neuronal data. For example, in schizophrenia patients the clinical phenomenon of aberrant salience has been described as learning irrelevant information or cognitive deficits have been linked to connectivity changes in frontoparietal networks. Computational psychiatry can make important contributions to the prediction of individual clinical courses as well as to a mechanistic understanding of psychiatric symptoms. For this a further development of reliable and valid methods across different disciplines is indispensable.
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Affiliation(s)
- Jakob Kaminski
- Klinik für Psychiatrie und Psychotherapie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Campus Mitte, Charitéplatz 1, 10117, Berlin, Deutschland.,Berlin Institute of Health, Berlin, Deutschland
| | - Teresa Katthagen
- Klinik für Psychiatrie und Psychotherapie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Campus Mitte, Charitéplatz 1, 10117, Berlin, Deutschland
| | - Florian Schlagenhauf
- Klinik für Psychiatrie und Psychotherapie, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Campus Mitte, Charitéplatz 1, 10117, Berlin, Deutschland. .,Bernstein Center for Computational Neuroscience, Berlin, Deutschland.
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6
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Oba T, Katahira K, Ohira H. The Effect of Reduced Learning Ability on Avoidance in Psychopathy: A Computational Approach. Front Psychol 2019; 10:2432. [PMID: 31736830 PMCID: PMC6838140 DOI: 10.3389/fpsyg.2019.02432] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 10/14/2019] [Indexed: 02/01/2023] Open
Abstract
Individuals with psychopathy often show deficits in learning, which often have negative consequences. Several theories have been proposed to explain psychopathic behaviors, but the learning mechanisms in psychopathy are still unclear. To clarify the learning anomalies in psychopathy, we fitted reinforcement learning (RL) models to behavioral data. We conducted two experiments to examine the effect of psychopathy as a group difference (Experiment 1) and as a continuum (Experiment 2). Forty-three undergraduates (in Experiment 1) and fifty-five undergraduate and graduate students (in Experiment 2) performed a go/no-go based learning task with accompanying rewards or punishments. Although we observed no differences in learning performance among the levels of psychopathic traits, the learning rate for the positive prediction error in the loss domain was lower for those with high-psychopathic trait than for those with low-psychopathic trait. This finding indicates that individuals with high-psychopathic traits update an action value less when they avoid a negative outcome. Our model can represent previous theories under a computational framework and provide a new perspective on impaired learning in psychopathy.
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Affiliation(s)
- Takeyuki Oba
- Department of Psychology, Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan
| | - Kentaro Katahira
- Department of Psychology, Graduate School of Informatics, Nagoya University, Nagoya, Japan
| | - Hideki Ohira
- Department of Psychology, Graduate School of Informatics, Nagoya University, Nagoya, Japan
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7
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Green MF, Horan WP, Lee J. Nonsocial and social cognition in schizophrenia: current evidence and future directions. World Psychiatry 2019; 18:146-161. [PMID: 31059632 PMCID: PMC6502429 DOI: 10.1002/wps.20624] [Citation(s) in RCA: 330] [Impact Index Per Article: 66.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Cognitive impairment in schizophrenia involves a broad array of nonsocial and social cognitive domains. It is a core feature of the illness, and one with substantial implications for treatment and prognosis. Our understanding of the causes, consequences and interventions for cognitive impairment in schizophrenia has grown substantially in recent years. Here we review a range of topics, including: a) the types of nonsocial cognitive, social cognitive, and perceptual deficits in schizophrenia; b) how deficits in schizophrenia are similar or different from those in other disorders; c) cognitive impairments in the prodromal period and over the lifespan in schizophrenia; d) neuroimaging of the neural substrates of nonsocial and social cognition, and e) relationships of nonsocial and social cognition to functional outcome. The paper also reviews the considerable efforts that have been directed to improve cognitive impairments in schizophrenia through novel psychopharmacology, cognitive remediation, social cognitive training, and alternative approaches. In the final section, we consider areas that are emerging and have the potential to provide future insights, including the interface of motivation and cognition, the influence of childhood adversity, metacognition, the role of neuroinflammation, computational modelling, the application of remote digital technology, and novel methods to evaluate brain network organization. The study of cognitive impairment has provided a way to approach, examine and comprehend a wide range of features of schizophrenia, and it may ultimately affect how we define and diagnose this complex disorder.
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Affiliation(s)
- Michael F. Green
- Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral SciencesUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA,Desert Pacific Mental Illness Research, Education and Clinical CenterVeterans Affairs Greater Los Angeles Healthcare SystemLos AngelesCAUSA,Veterans Affairs Program for Enhancing Community Integration for Homeless VeteransLos AngelesCAUSA
| | - William P. Horan
- Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral SciencesUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA,Desert Pacific Mental Illness Research, Education and Clinical CenterVeterans Affairs Greater Los Angeles Healthcare SystemLos AngelesCAUSA,Veterans Affairs Program for Enhancing Community Integration for Homeless VeteransLos AngelesCAUSA
| | - Junghee Lee
- Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral SciencesUniversity of California, Los Angeles (UCLA)Los AngelesCAUSA,Desert Pacific Mental Illness Research, Education and Clinical CenterVeterans Affairs Greater Los Angeles Healthcare SystemLos AngelesCAUSA,Veterans Affairs Program for Enhancing Community Integration for Homeless VeteransLos AngelesCAUSA
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8
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Rae CL, Critchley HD, Seth AK. A Bayesian Account of the Sensory-Motor Interactions Underlying Symptoms of Tourette Syndrome. Front Psychiatry 2019; 10:29. [PMID: 30890965 PMCID: PMC6412155 DOI: 10.3389/fpsyt.2019.00029] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Accepted: 01/17/2019] [Indexed: 11/17/2022] Open
Abstract
Tourette syndrome is a hyperkinetic movement disorder. Characteristic features include tics, recurrent movements that are experienced as compulsive and "unwilled"; uncomfortable premonitory sensations that resolve through tic release; and often, the ability to suppress tics temporarily. We demonstrate how these symptoms and features can be understood in terms of aberrant predictive (Bayesian) processing in hierarchical neural systems, explaining specifically: why tics arise, their "unvoluntary" nature, how premonitory sensations emerge, and why tic suppression works-sometimes. In our model, premonitory sensations and tics are generated through over-precise priors for sensation and action within somatomotor regions of the striatum. Abnormally high precision of priors arises through the dysfunctional synaptic integration of cortical inputs. These priors for sensation and action are projected into primary sensory and motor areas, triggering premonitory sensations and tics, which in turn elicit prediction errors for unexpected feelings and movements. We propose experimental paradigms to validate this Bayesian account of tics. Our model integrates behavioural, neuroimaging, and computational approaches to provide mechanistic insight into the pathophysiological basis of Tourette syndrome.
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Affiliation(s)
- Charlotte L. Rae
- Sackler Centre for Consciousness Science, University of Sussex, Brighton, United Kingdom
- Department of Neuroscience, Brighton and Sussex Medical School, Brighton, United Kingdom
| | - Hugo D. Critchley
- Sackler Centre for Consciousness Science, University of Sussex, Brighton, United Kingdom
- Department of Neuroscience, Brighton and Sussex Medical School, Brighton, United Kingdom
- Sussex Partnership NHS Foundation Trust, Brighton, United Kingdom
| | - Anil K. Seth
- Sackler Centre for Consciousness Science, University of Sussex, Brighton, United Kingdom
- School of Engineering and Informatics, University of Sussex, Brighton, United Kingdom
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9
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Stewart JL. A Computational Modeling Approach Supports Negative Reinforcement Theories of Addiction. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 4:218-219. [PMID: 30827480 DOI: 10.1016/j.bpsc.2019.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Accepted: 01/16/2019] [Indexed: 11/29/2022]
Affiliation(s)
- Jennifer L Stewart
- Laureate Institute for Brain Research, Tulsa, and the Department of Community Medicine, University of Tulsa, Tulsa, Oklahoma.
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10
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Shahar N, Hauser TU, Moutoussis M, Moran R, Keramati M, Dolan RJ. Improving the reliability of model-based decision-making estimates in the two-stage decision task with reaction-times and drift-diffusion modeling. PLoS Comput Biol 2019; 15:e1006803. [PMID: 30759077 PMCID: PMC6391008 DOI: 10.1371/journal.pcbi.1006803] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 02/26/2019] [Accepted: 01/17/2019] [Indexed: 01/10/2023] Open
Abstract
A well-established notion in cognitive neuroscience proposes that multiple brain systems contribute to choice behaviour. These include: (1) a model-free system that uses values cached from the outcome history of alternative actions, and (2) a model-based system that considers action outcomes and the transition structure of the environment. The widespread use of this distinction, across a range of applications, renders it important to index their distinct influences with high reliability. Here we consider the two-stage task, widely considered as a gold standard measure for the contribution of model-based and model-free systems to human choice. We tested the internal/temporal stability of measures from this task, including those estimated via an established computational model, as well as an extended model using drift-diffusion. Drift-diffusion modeling suggested that both choice in the first stage, and RTs in the second stage, are directly affected by a model-based/free trade-off parameter. Both parameter recovery and the stability of model-based estimates were poor but improved substantially when both choice and RT were used (compared to choice only), and when more trials (than conventionally used in research practice) were included in our analysis. The findings have implications for interpretation of past and future studies based on the use of the two-stage task, as well as for characterising the contribution of model-based processes to choice behaviour.
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Affiliation(s)
- Nitzan Shahar
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, United Kingdom
| | - Tobias U. Hauser
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, United Kingdom
| | - Michael Moutoussis
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, United Kingdom
| | - Rani Moran
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, United Kingdom
| | - Mehdi Keramati
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, United Kingdom
| | | | - Raymond J. Dolan
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London, United Kingdom
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11
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Cuthbert BN. The PRISM project: Social withdrawal from an RDoC perspective. Neurosci Biobehav Rev 2019; 97:34-37. [DOI: 10.1016/j.neubiorev.2018.08.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Revised: 08/02/2018] [Accepted: 08/10/2018] [Indexed: 11/26/2022]
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12
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McDermott TJ, Kirlic N, Aupperle RL. Roadmap for optimizing the clinical utility of emotional stress paradigms in human neuroimaging research. Neurobiol Stress 2018; 8:134-146. [PMID: 29888309 PMCID: PMC5991342 DOI: 10.1016/j.ynstr.2018.05.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2017] [Revised: 04/30/2018] [Accepted: 05/03/2018] [Indexed: 01/24/2023] Open
Abstract
The emotional stress response is relevant to a number of psychiatric disorders, including posttraumatic stress disorder (PTSD) in particular. Research using neuroimaging methods such as functional magnetic resonance imaging (fMRI) to probe stress-related neural processing have provided some insights into psychiatric disorders. Treatment providers and individual patients would benefit from clinically useful fMRI paradigms that provide information about patients' current brain state and responses to stress in order to inform the treatment selection process. However, neuroimaging has not yet made a meaningful impact on real-world clinical practice. This lack of clinical utility may be related to a number of basic psychometric properties that are often overlooked during fMRI task development. The goals of the current review are to discuss important methodological considerations for current human fMRI stress-related paradigms and to provide a roadmap for developing methodologically sound and clinically useful paradigms. This would include establishing various aspects of reliability, including internal consistency, test-retest and multi-site, as well as validity, including face, content, construct, and criterion. In addition, the establishment of standardized normative data from a large sample of participants would support our understanding of how any one individual compares to the general population. Addressing these methodological gaps will likely have a powerful effect on improving the replicability of findings and optimize our chances for improving real-world clinical outcomes.
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Affiliation(s)
- Timothy J. McDermott
- Laureate Institute for Brain Research, Tulsa, OK, United States
- Department of Psychology, University of Tulsa, Tulsa, OK, United States
| | - Namik Kirlic
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Robin L. Aupperle
- Laureate Institute for Brain Research, Tulsa, OK, United States
- Department of Community Medicine, University of Tulsa, Tulsa, OK, United States
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13
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Premonitory urges and tics in Tourette syndrome: computational mechanisms and neural correlates. Curr Opin Neurobiol 2017; 46:187-199. [PMID: 29017141 DOI: 10.1016/j.conb.2017.08.009] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 08/01/2017] [Accepted: 08/21/2017] [Indexed: 12/22/2022]
Abstract
Tourette syndrome is characterized by open motor behaviors - tics - but another crucial aspect of the disorder is the presence of premonitory urges: uncomfortable sensations that typically precede tics and are temporarily alleviated by tics. We review the evidence implicating the somatosensory cortices and the insula in premonitory urges and the motor cortico-basal ganglia-thalamo-cortical loop in tics. We consider how these regions interact during tic execution, suggesting that the insula plays an important role as a nexus linking the sensory and emotional character of premonitory urges with their translation into tics. We also consider how these regions interact during tic learning, integrating the neural evidence with a computational perspective on how premonitory-urge alleviation reinforces tics.
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Aponte EA, Schöbi D, Stephan KE, Heinzle J. The Stochastic Early Reaction, Inhibition, and late Action (SERIA) model for antisaccades. PLoS Comput Biol 2017; 13:e1005692. [PMID: 28767650 PMCID: PMC5555715 DOI: 10.1371/journal.pcbi.1005692] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2017] [Revised: 08/14/2017] [Accepted: 07/20/2017] [Indexed: 01/19/2023] Open
Abstract
The antisaccade task is a classic paradigm used to study the voluntary control of eye movements. It requires participants to suppress a reactive eye movement to a visual target and to concurrently initiate a saccade in the opposite direction. Although several models have been proposed to explain error rates and reaction times in this task, no formal model comparison has yet been performed. Here, we describe a Bayesian modeling approach to the antisaccade task that allows us to formally compare different models on the basis of their evidence. First, we provide a formal likelihood function of actions (pro- and antisaccades) and reaction times based on previously published models. Second, we introduce the Stochastic Early Reaction, Inhibition, and late Action model (SERIA), a novel model postulating two different mechanisms that interact in the antisaccade task: an early GO/NO-GO race decision process and a late GO/GO decision process. Third, we apply these models to a data set from an experiment with three mixed blocks of pro- and antisaccade trials. Bayesian model comparison demonstrates that the SERIA model explains the data better than competing models that do not incorporate a late decision process. Moreover, we show that the early decision process postulated by the SERIA model is, to a large extent, insensitive to the cue presented in a single trial. Finally, we use parameter estimates to demonstrate that changes in reaction time and error rate due to the probability of a trial type (pro- or antisaccade) are best explained by faster or slower inhibition and the probability of generating late voluntary prosaccades.
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Affiliation(s)
- Eduardo A. Aponte
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & Swiss Institute of Technology Zurich, Zurich, Switzerland
- * E-mail: (EAA); (JH)
| | - Dario Schöbi
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & Swiss Institute of Technology Zurich, Zurich, Switzerland
| | - Klaas E. Stephan
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & Swiss Institute of Technology Zurich, Zurich, Switzerland
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
| | - Jakob Heinzle
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & Swiss Institute of Technology Zurich, Zurich, Switzerland
- * E-mail: (EAA); (JH)
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