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Hagan KE, Aimufua I, Haynos AF, Walsh BT. The explore/exploit trade-off: An ecologically valid and translational framework that can advance mechanistic understanding of eating disorders. Int J Eat Disord 2024; 57:1102-1108. [PMID: 38385592 DOI: 10.1002/eat.24173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/26/2024] [Accepted: 02/08/2024] [Indexed: 02/23/2024]
Abstract
The explore/exploit trade-off is a decision-making process that is conserved across species and balances exploring unfamiliar choices of unknown value with choosing familiar options of known value to maximize reward. This framework is rooted in behavioral ecology and has traditionally been used to study maladaptive versus adaptive non-human animal foraging behavior. Researchers have begun to recognize the potential utility of understanding human decision-making and psychopathology through the explore/exploit trade-off. In this article, we propose that explore/exploit trade-off holds promise for advancing our mechanistic understanding of decision-making processes that confer vulnerability for and maintain eating pathology due to its neurodevelopmental bases, conservation across species, and ability to be mathematically modeled. We present a model for how suboptimal explore/exploit decision-making can promote disordered eating and present recommendations for future research applying this framework to eating pathology. Taken together, the explore/exploit trade-off provides a translational framework for expanding etiologic and maintenance models of eating pathology, given developmental changes in explore/exploit decision-making that coincide in time with the emergence of eating pathology and evidence of biased explore/exploit decision-making in psychopathology. Additionally, understanding explore/exploit decision-making in eating disorders may improve knowledge of their underlying pathophysiology, informing targeted clinical interventions such as neuromodulation and pharmacotherapy. PUBLIC SIGNIFICANCE STATEMENT: The explore/exploit trade-off is a cross-species decision-making process whereby organisms choose between a known option with a known reward or sampling unfamiliar options. We hypothesize that imbalanced explore/exploit decision-making can promote disordered eating and present preliminary data. We propose that explore/exploit trade-off has significant potential to advance understanding of the neurocognitive and neurodevelopmental mechanisms of eating pathology, which could ultimately guide revisions of etiologic models and inform novel interventions.
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Affiliation(s)
- Kelsey E Hagan
- Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia, USA
- Institute for Women's Health, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Ivieosa Aimufua
- Department of Psychiatry, New York State Psychiatric Institute, Columbia University Irving Medical Center, New York, New York, USA
| | - Ann F Haynos
- Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia, USA
- Department of Psychology, Virginia Commonwealth University, Richmond, Virginia, USA
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota, USA
| | - B Timothy Walsh
- Department of Psychiatry, New York State Psychiatric Institute, Columbia University Irving Medical Center, New York, New York, USA
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Powers A, Angelos P, Bond A, Farina E, Fredericks C, Gandhi J, Greenwald M, Hernandez-Busot G, Hosein G, Kelley M, Mourgues C, Palmer W, Rodriguez-Sanchez J, Seabury R, Toribio S, Vin R, Weleff J, Benrimoh D. A computational account of the development and evolution of psychotic symptoms. ArXiv 2024:arXiv:2404.10954v1. [PMID: 38699166 PMCID: PMC11065053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
The mechanisms of psychotic symptoms like hallucinations and delusions are often investigated in fully-formed illness, well after symptoms emerge. These investigations have yielded key insights, but are not well-positioned to reveal the dynamic forces underlying symptom formation itself. Understanding symptom development over time would allow us to identify steps in the pathophysiological process leading to psychosis, shifting the focus of psychiatric intervention from symptom alleviation to prevention. We propose a model for understanding the emergence of psychotic symptoms within the context of an adaptive, developing neural system. We will make the case for a pathophysiological process that begins with cortical hyperexcitability and bottom-up noise transmission, which engenders inappropriate belief formation via aberrant prediction error signaling. We will argue that this bottom-up noise drives learning about the (im)precision of new incoming sensory information because of diminished signal-to-noise ratio, causing an adaptive relative over-reliance on prior beliefs. This over-reliance on priors predisposes to hallucinations and covaries with hallucination severity. An over-reliance on priors may also lead to increased conviction in the beliefs generated by bottom-up noise and drive movement toward conversion to psychosis. We will identify predictions of our model at each stage, examine evidence to support or refute those predictions, and propose experiments that could falsify or help select between alternative elements of the overall model. Nesting computational abnormalities within longitudinal development allows us to account for hidden dynamics among the mechanisms driving symptom formation and to view established symptomatology as a point of equilibrium among competing biological forces.
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Affiliation(s)
- Albert Powers
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Philip Angelos
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Alexandria Bond
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Emily Farina
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Carolyn Fredericks
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Jay Gandhi
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Maximillian Greenwald
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | | | - Gabriel Hosein
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Megan Kelley
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Catalina Mourgues
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - William Palmer
- Yale University Department of Psychology, New Haven, CT USA
| | | | - Rashina Seabury
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Silmilly Toribio
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Raina Vin
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Jeremy Weleff
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - David Benrimoh
- Department of Psychiatry, McGill University, Montreal, Canada
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Story GW, Smith R, Moutoussis M, Berwian IM, Nolte T, Bilek E, Siegel JZ, Dolan RJ. A social inference model of idealization and devaluation. Psychol Rev 2024; 131:749-780. [PMID: 37602986 PMCID: PMC11114086 DOI: 10.1037/rev0000430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 01/31/2023] [Accepted: 03/14/2023] [Indexed: 08/22/2023]
Abstract
People often form polarized beliefs, imbuing objects (e.g., themselves or others) with unambiguously positive or negative qualities. In clinical settings, this is referred to as dichotomous thinking or "splitting" and is a feature of several psychiatric disorders. Here, we introduce a Bayesian model of splitting that parameterizes a tendency to rigidly categorize objects as either entirely "Bad" or "Good," rather than to flexibly learn dispositions along a continuous scale. Distinct from the previous descriptive theories, the model makes quantitative predictions about how dichotomous beliefs emerge and are updated in light of new information. Specifically, the model addresses how splitting is context-dependent, yet exhibits stability across time. A key model feature is that phases of devaluation and/or idealization are consolidated by rationally attributing counter-evidence to external factors. For example, when another person is idealized, their less-than-perfect behavior is attributed to unfavorable external circumstances. However, sufficient counter-evidence can trigger switches of polarity, producing bistable dynamics. We show that the model can be fitted to empirical data, to measure individual susceptibility to relational instability. For example, we find that a latent categorical belief that others are "Good" accounts for less changeable, and more certain, character impressions of benevolent as opposed to malevolent others among healthy participants. By comparison, character impressions made by participants with borderline personality disorder reveal significantly higher and more symmetric splitting. The generative framework proposed invites applications for modeling oscillatory relational and affective dynamics in psychotherapeutic contexts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Affiliation(s)
| | | | - Michael Moutoussis
- Max Planck-University College London Centre for Computational Psychiatry and Ageing Research, University College London
| | | | - Tobias Nolte
- Wellcome Centre for Human Neuroimaging, University College London
| | - Edda Bilek
- Wellcome Centre for Human Neuroimaging, University College London
| | - Jenifer Z Siegel
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University
| | - Raymond J Dolan
- Max Planck-University College London Centre for Computational Psychiatry and Ageing Research, University College London
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Kilshaw RE, Boggins A, Everett O, Butner E, Leifker FR, Baucom BRW. Benchmarking Mental Health Status Using Passive Sensor Data: Protocol for a Prospective Observational Study. JMIR Res Protoc 2024; 13:e53857. [PMID: 38536220 PMCID: PMC11007613 DOI: 10.2196/53857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 01/27/2024] [Accepted: 02/22/2024] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND Computational psychiatry has the potential to advance the diagnosis, mechanistic understanding, and treatment of mental health conditions. Promising results from clinical samples have led to calls to extend these methods to mental health risk assessment in the general public; however, data typically used with clinical samples are neither available nor scalable for research in the general population. Digital phenotyping addresses this by capitalizing on the multimodal and widely available data created by sensors embedded in personal digital devices (eg, smartphones) and is a promising approach to extending computational psychiatry methods to improve mental health risk assessment in the general population. OBJECTIVE Building on recommendations from existing computational psychiatry and digital phenotyping work, we aim to create the first computational psychiatry data set that is tailored to studying mental health risk in the general population; includes multimodal, sensor-based behavioral features; and is designed to be widely shared across academia, industry, and government using gold standard methods for privacy, confidentiality, and data integrity. METHODS We are using a stratified, random sampling design with 2 crossed factors (difficulties with emotion regulation and perceived life stress) to recruit a sample of 400 community-dwelling adults balanced across high- and low-risk for episodic mental health conditions. Participants first complete self-report questionnaires assessing current and lifetime psychiatric and medical diagnoses and treatment, and current psychosocial functioning. Participants then complete a 7-day in situ data collection phase that includes providing daily audio recordings, passive sensor data collected from smartphones, self-reports of daily mood and significant events, and a verbal description of the significant daily events during a nightly phone call. Participants complete the same baseline questionnaires 6 and 12 months after this phase. Self-report questionnaires will be scored using standard methods. Raw audio and passive sensor data will be processed to create a suite of daily summary features (eg, time spent at home). RESULTS Data collection began in June 2022 and is expected to conclude by July 2024. To date, 310 participants have consented to the study; 149 have completed the baseline questionnaire and 7-day intensive data collection phase; and 61 and 31 have completed the 6- and 12-month follow-up questionnaires, respectively. Once completed, the proposed data set will be made available to academic researchers, industry, and the government using a stepped approach to maximize data privacy. CONCLUSIONS This data set is designed as a complementary approach to current computational psychiatry and digital phenotyping research, with the goal of advancing mental health risk assessment within the general population. This data set aims to support the field's move away from siloed research laboratories collecting proprietary data and toward interdisciplinary collaborations that incorporate clinical, technical, and quantitative expertise at all stages of the research process. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/53857.
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Affiliation(s)
- Robyn E Kilshaw
- Department of Psychology, University of Utah, Salt Lake City, UT, United States
| | - Abigail Boggins
- Department of Psychology, University of Utah, Salt Lake City, UT, United States
| | - Olivia Everett
- Department of Psychology, University of Utah, Salt Lake City, UT, United States
| | - Emma Butner
- Department of Psychology, University of Utah, Salt Lake City, UT, United States
| | - Feea R Leifker
- Department of Psychology, University of Utah, Salt Lake City, UT, United States
| | - Brian R W Baucom
- Department of Psychology, University of Utah, Salt Lake City, UT, United States
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Prescott TJ, Montes González FM, Gurney K, Humphries MD, Redgrave P. Simulated Dopamine Modulation of a Neurorobotic Model of the Basal Ganglia. Biomimetics (Basel) 2024; 9:139. [PMID: 38534824 DOI: 10.3390/biomimetics9030139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 02/20/2024] [Accepted: 02/21/2024] [Indexed: 03/28/2024] Open
Abstract
The vertebrate basal ganglia play an important role in action selection-the resolution of conflicts between alternative motor programs. The effective operation of basal ganglia circuitry is also known to rely on appropriate levels of the neurotransmitter dopamine. We investigated reducing or increasing the tonic level of simulated dopamine in a prior model of the basal ganglia integrated into a robot control architecture engaged in a foraging task inspired by animal behaviour. The main findings were that progressive reductions in the levels of simulated dopamine caused slowed behaviour and, at low levels, an inability to initiate movement. These states were partially relieved by increased salience levels (stronger sensory/motivational input). Conversely, increased simulated dopamine caused distortion of the robot's motor acts through partially expressed motor activity relating to losing actions. This could also lead to an increased frequency of behaviour switching. Levels of simulated dopamine that were either significantly lower or higher than baseline could cause a loss of behavioural integration, sometimes leaving the robot in a 'behavioral trap'. That some analogous traits are observed in animals and humans affected by dopamine dysregulation suggests that robotic models could prove useful in understanding the role of dopamine neurotransmission in basal ganglia function and dysfunction.
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Affiliation(s)
- Tony J Prescott
- Department of Computer Science, University of Sheffield, Sheffield S10 2TN, UK
| | | | - Kevin Gurney
- Department of Psychology, University of Sheffield, Sheffield S10 2TN, UK
| | - Mark D Humphries
- School of Psychology, University of Nottingham, Nottingham NG7 2RD, UK
| | - Peter Redgrave
- Department of Psychology, University of Sheffield, Sheffield S10 2TN, UK
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Villano WJ, Heller AS. Depression is associated with blunted affective responses to naturalistic reward prediction errors. Psychol Med 2024:1-9. [PMID: 38305099 DOI: 10.1017/s0033291724000047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
BACKGROUND Depression is characterized by abnormalities in emotional processing, but the specific drivers of such emotional abnormalities are unknown. Computational work indicates that both surprising outcomes (prediction errors; PEs) and outcomes (values) themselves drive emotional responses, but neither has been consistently linked to affective disturbances in depression. As a result, the computational mechanisms driving emotional abnormalities in depression remain unknown. METHODS Here, in 687 individuals, one-third of whom qualify as depressed via a standard self-report measure (the PHQ-9), we use high-stakes, naturalistic events - the reveal of midterm exam grades - to test whether individuals with heightened depression display a specific reduction in emotional response to positive PEs. RESULTS Using Bayesian mixed effects models, we find that individuals with heightened depression do not affectively benefit from surprising, good outcomes - that is, they display reduced affective responses to positive PEs. These results were highly specific: effects were not observed to negative PEs, value signals (grades), and were not related to generalized anxiety. This suggests that the computational drivers of abnormalities in emotion in depression may be specifically due to positive PE-based emotional responding. CONCLUSIONS Affective abnormalities are core depression symptoms, but the computational mechanisms underlying such differences are unknown. This work suggests that blunted affective reactions to positive PEs are likely mechanistic drivers of emotional dysregulation in depression.
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Affiliation(s)
- William J Villano
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Aaron S Heller
- Department of Psychology, University of Miami, Coral Gables, FL, USA
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7
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Ging-Jehli NR, Painter QA, Kraemer HA, Roley-Roberts ME, Panchyshyn C, deBeus R, Arnold LE. A diffusion decision model analysis of the cognitive effects of neurofeedback for ADHD. Neuropsychology 2024; 38:146-156. [PMID: 37971859 PMCID: PMC10842533 DOI: 10.1037/neu0000932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023] Open
Abstract
OBJECTIVE To examine cognitive effects of neurofeedback (NF) for attention-deficit hyperactivity disorder (ADHD) as a secondary outcome of a randomized clinical trial. METHOD In a double-blind randomized clinical trial (NCT02251743), 133 7-10-year olds with ADHD received either 38 sessions of NF (n = 78) or control treatment (n = 55) and performed an integrated visual and auditory continuous performance test at baseline, mid- and end-treatment. We used the diffusion decision model to decompose integrated visual and auditory continuous performance test performance at each assessment into cognitive components: efficiency of integrating stimulus information (v), context sensitivity (cv), response cautiousness (a), response bias (z/a), and nondecision time for perceptual encoding and response execution (Ter). Based on prior findings, we tested whether the components known to be deficient improved with NF and explored whether other cognitive components improved using linear mixed modeling. RESULTS Before NF, children with ADHD showed main deficits in integrating stimulus information (v), which led to less accurate and slower responses than healthy controls (p = .008). The NF group showed significantly more improvement in integrating auditory stimulus information (v) than control treatment (significant group-by-time-by-modality effect: p = .044). CONCLUSIONS NF seems to improve v, deficient in ADHD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Affiliation(s)
- Nadja R. Ging-Jehli
- Department of Psychology, The Ohio State University, Columbus OH
- Carney Institute for Brain Science, Department of Cognitive, Linguistic, & Psychological Sciences, Brown University, Providence, RI
| | | | - Helena A. Kraemer
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Cupertino, CA 95014, USA
| | | | | | - Roger deBeus
- Department of Psychology, University of North Carolina at Asheville
| | - L. Eugene Arnold
- Department of Psychiatry and Behavioral Health, Nisonger Center UCEDD, The Ohio State University
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8
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Cisler JM, Dunsmoor JE, Fonzo GA, Nemeroff CB. Latent-state and model-based learning in PTSD. Trends Neurosci 2024; 47:150-162. [PMID: 38212163 PMCID: PMC10923154 DOI: 10.1016/j.tins.2023.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 12/18/2023] [Accepted: 12/18/2023] [Indexed: 01/13/2024]
Abstract
Post-traumatic stress disorder (PTSD) is characterized by altered emotional and behavioral responding following a traumatic event. In this article, we review the concepts of latent-state and model-based learning (i.e., learning and inferring abstract task representations) and discuss their relevance for clinical and neuroscience models of PTSD. Recent data demonstrate evidence for brain and behavioral biases in these learning processes in PTSD. These new data potentially recast excessive fear towards trauma cues as a problem in learning and updating abstract task representations, as opposed to traditional conceptualizations focused on stimulus-specific learning. Biases in latent-state and model-based learning may also be a common mechanism targeted in common therapies for PTSD. We highlight key knowledge gaps that need to be addressed to further elaborate how latent-state learning and its associated neurocircuitry mechanisms function in PTSD and how to optimize treatments to target these processes.
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Affiliation(s)
- Josh M Cisler
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin, Austin, TX, USA; Institute for Early Life Adversity Research, University of Texas at Austin, Austin, TX, USA.
| | - Joseph E Dunsmoor
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin, Austin, TX, USA; Institute for Early Life Adversity Research, University of Texas at Austin, Austin, TX, USA
| | - Gregory A Fonzo
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin, Austin, TX, USA; Institute for Early Life Adversity Research, University of Texas at Austin, Austin, TX, USA
| | - Charles B Nemeroff
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin, Austin, TX, USA; Institute for Early Life Adversity Research, University of Texas at Austin, Austin, TX, USA
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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. Res Sq 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Kirschner H, Nassar MR, Fischer AG, Frodl T, Meyer-Lotz G, Froböse S, Seidenbecher S, Klein TA, Ullsperger M. Transdiagnostic inflexible learning dynamics explain deficits in depression and schizophrenia. Brain 2024; 147:201-214. [PMID: 38058203 PMCID: PMC10766268 DOI: 10.1093/brain/awad362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 09/25/2023] [Accepted: 10/10/2023] [Indexed: 12/08/2023] Open
Abstract
Deficits in reward learning are core symptoms across many mental disorders. Recent work suggests that such learning impairments arise by a diminished ability to use reward history to guide behaviour, but the neuro-computational mechanisms through which these impairments emerge remain unclear. Moreover, limited work has taken a transdiagnostic approach to investigate whether the psychological and neural mechanisms that give rise to learning deficits are shared across forms of psychopathology. To provide insight into this issue, we explored probabilistic reward learning in patients diagnosed with major depressive disorder (n = 33) or schizophrenia (n = 24) and 33 matched healthy controls by combining computational modelling and single-trial EEG regression. In our task, participants had to integrate the reward history of a stimulus to decide whether it is worthwhile to gamble on it. Adaptive learning in this task is achieved through dynamic learning rates that are maximal on the first encounters with a given stimulus and decay with increasing stimulus repetitions. Hence, over the course of learning, choice preferences would ideally stabilize and be less susceptible to misleading information. We show evidence of reduced learning dynamics, whereby both patient groups demonstrated hypersensitive learning (i.e. less decaying learning rates), rendering their choices more susceptible to misleading feedback. Moreover, there was a schizophrenia-specific approach bias and a depression-specific heightened sensitivity to disconfirmational feedback (factual losses and counterfactual wins). The inflexible learning in both patient groups was accompanied by altered neural processing, including no tracking of expected values in either patient group. Taken together, our results thus provide evidence that reduced trial-by-trial learning dynamics reflect a convergent deficit across depression and schizophrenia. Moreover, we identified disorder distinct learning deficits.
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Affiliation(s)
- Hans Kirschner
- Institute of Psychology, Otto-von-Guericke University, D-39106 Magdeburg, Germany
| | - Matthew R Nassar
- Robert J. and Nancy D. Carney Institute for Brain Science, Brown University, Providence, RI 02912-1821, USA
- Department of Neuroscience, Brown University, Providence, RI 02912-1821, USA
| | - Adrian G Fischer
- Department of Education and Psychology, Freie Universität Berlin, D-14195 Berlin, Germany
| | - Thomas Frodl
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, D-39106 Magdeburg, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen 52074, Germany
- German Center for Mental Health (DZPG), D-39106 Magdeburg, Germany
- Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, D-39106 Magdeburg, Germany
| | - Gabriela Meyer-Lotz
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, D-39106 Magdeburg, Germany
| | - Sören Froböse
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, D-39106 Magdeburg, Germany
| | - Stephanie Seidenbecher
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, D-39106 Magdeburg, Germany
| | - Tilmann A Klein
- Institute of Psychology, Otto-von-Guericke University, D-39106 Magdeburg, Germany
- Center for Behavioral Brain Sciences, D-39106 Magdeburg, Germany
| | - Markus Ullsperger
- Institute of Psychology, Otto-von-Guericke University, D-39106 Magdeburg, Germany
- German Center for Mental Health (DZPG), D-39106 Magdeburg, Germany
- Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, D-39106 Magdeburg, Germany
- Center for Behavioral Brain Sciences, D-39106 Magdeburg, Germany
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McInnes AN, Sullivan CRP, MacDonald AW, Widge AS. Psychometric validation and clinical correlates of an experiential foraging task. bioRxiv 2023:2023.12.28.573439. [PMID: 38234810 PMCID: PMC10793407 DOI: 10.1101/2023.12.28.573439] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Measuring the function of decision-making systems is a central goal of computational psychiatry. Individual measures of decisional function could be used to describe neurocognitive profiles that underpin psychopathology and offer insights into deficits that are shared across traditional diagnostic classes. However, there are few demonstrably reliable and mechanistically relevant metrics of decision making that can accurately capture the complex overlapping domains of cognition whilst also quantifying the heterogeneity of function between individuals. The WebSurf task is a reverse-translational human experiential foraging paradigm which indexes naturalistic and clinically relevant decision-making. To determine its potential clinical utility, we examined the psychometric properties and clinical correlates of behavioural parameters extracted from WebSurf in an initial exploratory experiment and a pre-registered validation experiment. Behaviour was stable over repeated administrations of the task, as were individual differences. The ability to measure decision making consistently supports the potential utility of the task in predicting an individual's propensity for response to psychiatric treatment, in evaluating clinical change during treatment, and in defining neurocognitive profiles that relate to psychopathology. Specific aspects of WebSurf behaviour also correlate with anhedonic and externalising symptoms. Importantly, these behavioural parameters may measure dimensions of psychological variance that are not captured by traditional rating scales. WebSurf and related paradigms might therefore be useful platforms for computational approaches to precision psychiatry.
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Affiliation(s)
- Aaron N. McInnes
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | - Christi R. P. Sullivan
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | | | - Alik S. Widge
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
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Bustamante LA, Oshinowo T, Lee JR, Tong E, Burton AR, Shenhav A, Cohen JD, Daw ND. Effort Foraging Task reveals positive correlation between individual differences in the cost of cognitive and physical effort in humans. Proc Natl Acad Sci U S A 2023; 120:e2221510120. [PMID: 38064507 PMCID: PMC10723129 DOI: 10.1073/pnas.2221510120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 10/26/2023] [Indexed: 12/17/2023] Open
Abstract
Effort-based decisions, in which people weigh potential future rewards against effort costs required to achieve those rewards involve both cognitive and physical effort, though the mechanistic relationship between them is not yet understood. Here, we use an individual differences approach to isolate and measure the computational processes underlying effort-based decisions and test the association between cognitive and physical domains. Patch foraging is an ecologically valid reward rate maximization problem with well-developed theoretical tools. We developed the Effort Foraging Task, which embedded cognitive or physical effort into patch foraging, to quantify the cost of both cognitive and physical effort indirectly, by their effects on foraging choices. Participants chose between harvesting a depleting patch, or traveling to a new patch that was costly in time and effort. Participants' exit thresholds (reflecting the reward they expected to receive by harvesting when they chose to travel to a new patch) were sensitive to cognitive and physical effort demands, allowing us to quantify the perceived effort cost in monetary terms. The indirect sequential choice style revealed effort-seeking behavior in a minority of participants (preferring high over low effort) that has apparently been missed by many previous approaches. Individual differences in cognitive and physical effort costs were positively correlated, suggesting that these are perceived and processed in common. We used canonical correlation analysis to probe the relationship of task measures to self-reported affect and motivation, and found correlations of cognitive effort with anxiety, cognitive function, behavioral activation, and self-efficacy, but no similar correlations with physical effort.
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Affiliation(s)
- Laura A. Bustamante
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ08544
- Department of Psychological and Brain Sciences, Washington University in Saint Louis, Saint Louis, MO63130
| | - Temitope Oshinowo
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ08544
| | - Jeremy R. Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ08544
| | - Elizabeth Tong
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ08544
| | - Allison R. Burton
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ08544
| | - Amitai Shenhav
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI02912
- Carney Institute for Brain Science, Brown University, Providence, RI02906
| | - Jonathan D. Cohen
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ08544
| | - Nathaniel D. Daw
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ08544
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Rządeczka M, Wodziński M, Moskalewicz M. Cognitive biases as an adaptive strategy in autism and schizophrenia spectrum: the compensation perspective on neurodiversity. Front Psychiatry 2023; 14:1291854. [PMID: 38116384 PMCID: PMC10729319 DOI: 10.3389/fpsyt.2023.1291854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 11/13/2023] [Indexed: 12/21/2023] Open
Abstract
This article presents a novel theoretical perspective on the role of cognitive biases within the autism and schizophrenia spectrum by integrating the evolutionary and computational approaches. Against the background of neurodiversity, cognitive biases are presented as primary adaptive strategies, while the compensation of their shortcomings is a potential cognitive advantage. The article delineates how certain subtypes of autism represent a unique cognitive strategy to manage cognitive biases at the expense of rapid and frugal heuristics. In contrast, certain subtypes of schizophrenia emerge as distinctive cognitive strategies devised to navigate social interactions, albeit with a propensity for overdetecting intentional behaviors. In conclusion, the paper emphasizes that while extreme manifestations might appear non-functional, they are merely endpoints of a broader, primarily functional spectrum of cognitive strategies. The central argument hinges on the premise that cognitive biases in both autism and schizophrenia spectrums serve as compensatory mechanisms tailored for specific ecological niches.
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Affiliation(s)
- Marcin Rządeczka
- Institute of Philosophy, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
- IDEAS NCBR, Warsaw, Poland
| | | | - Marcin Moskalewicz
- Institute of Philosophy, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
- IDEAS NCBR, Warsaw, Poland
- Philosophy of Mental Health Unit, Department of Social Sciences and the Humanities, Poznan University of Medical Sciences, Poznań, Poland
- Phenomenological Psychopathology and Psychotherapy, Psychiatric Clinic, University of Heidelberg, Heidelberg, Germany
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Mukherjee D, van Geen C, Kable J. Leveraging Decision Science to Characterize Depression. Curr Dir Psychol Sci 2023; 32:462-470. [PMID: 38313830 PMCID: PMC10836825 DOI: 10.1177/09637214231194962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
This brief review examines the potential to use decision science to objectively characterize depression. We provide a brief overview of the existing literature examining different domains of decision-making in depression. Because this overview highlights the specific role of reinforcement learning as an important decision process affected in the disorder, we then introduce reinforcement learning modeling and explain how this approach has identified specific reinforcement learning deficits in depression. We conclude with ideas for future research at the intersection of decision science and depression, emphasizing the potential for decision science to help uncover underlying mechanisms and targets for the treatment of depression.
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Affiliation(s)
- Dahlia Mukherjee
- Department of Psychiatry and Behavioral Health, Pennsylvania State University College of Medicine
- Milton S. Hershey Medical Center, Pennsylvania State University
| | | | - Joseph Kable
- Department of Psychology, University of Pennsylvania
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15
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Giotakos O. Editorial: From brain priorities to brain modeling. Front Psychiatry 2023; 14:1272054. [PMID: 37908597 PMCID: PMC10614046 DOI: 10.3389/fpsyt.2023.1272054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 10/04/2023] [Indexed: 11/02/2023] Open
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Vasilchenko KF, Chumakov EM. Current status, challenges and future prospects in computational psychiatry: a narrative review. Consort Psychiatr 2023; 4:33-42. [PMID: 38249533 PMCID: PMC10795945 DOI: 10.17816/cp11244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 09/12/2023] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Computational psychiatry is an area of scientific knowledge which lies at the intersection of neuroscience, psychiatry, and computer science. It employs mathematical models and computational simulations to shed light on the complexities inherent to mental disorders. AIM The aim of this narrative review is to offer insight into the current landscape of computational psychiatry, to discuss its significant challenges, as well as the potential opportunities for the fields growth. METHODS The authors have carried out a narrative review of the scientific literature published on the topic of computational psychiatry. The literature search was performed in the PubMed, eLibrary, PsycINFO, and Google Scholar databases. A descriptive analysis was used to summarize the published information on the theoretical and practical aspects of computational psychiatry. RESULTS The article relates the development of the scientific approach in computational psychiatry since the mid-1980s. The data on the practical application of computational psychiatry in modeling psychiatric disorders and explaining the mechanisms of how psychopathological symptomatology develops (in schizophrenia, attention-deficit/hyperactivity disorder, autism spectrum disorder, anxiety disorders, obsessive-compulsive disorder, substance use disorders) are summarized. Challenges, limitations, and the prospects of computational psychiatry are discussed. CONCLUSION The capacity of current computational technologies in psychiatry has reached a stage where its integration into psychiatric practice is not just feasible but urgently needed. The hurdles that now need to be addressed are no longer rooted in technological advancement, but in ethics, education, and understanding.
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Affiliation(s)
- Kirill F. Vasilchenko
- The Human artificial control Keren (HacK) lab, Azrieli Faculty of Medicine, Bar-Ilan University
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17
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Abstract
BACKGROUND AND HYPOTHESIS Humans develop a constellation of different representations of the external environment, even in the face of the same sensory exposure. According to the Bayesian framework, these differentiations could be grounded in a different weight assigned to prior knowledge vs. new external inputs in predictive inference. Since recent advances in computational psychiatry suggest that autism (ASD) and schizophrenia (SSD) lie on the two diametric poles of the same predictive continuum, the adoption of a specific inferential style could be routed by dispositional factors related to autistic and schizotypal traits. However, no studies have directly investigated the role of ASD-SSD dimension in shaping the neuro-behavioral markers underlying perceptual inference. STUDY DESIGN We used a probabilistic detection task while simultaneously recording EEG to investigate whether neurobehavioral signatures related to prior processing were diametrically shaped by ASD and SSD traits in the general population (n = 80). RESULTS We found that the position along the ASD-SSD continuum directed the predictive strategies adopted by the individuals in decision-making. While proximity to the positive schizotypy pole was associated with the adoption of the predictive approach associated to the hyper-weighting of prior knowledge, proximity to ASD pole was related to strategies that favored sensory evidence in decision-making. CONCLUSIONS These findings revealed that the weight assigned to prior knowledge is a marker of the ASD-SSD continuum, potentially useful for identifying individuals at-risk of developing mental disorders and for understanding the mechanisms contributing to the onset of symptoms observed in ASD and SSD clinical forms.
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Affiliation(s)
- Luca Tarasi
- Dipartimento di Psicologia, Alma Mater Studiorum – Università di Bologna, Centro Studi e Ricerche in Neuroscienze Cognitive, Campus di Cesena, via Rasi e Spinelli, 176, 47521 Cesena, Italy
| | - Maria Eugenia Martelli
- Dipartimento di Psicologia, Alma Mater Studiorum – Università di Bologna, Centro Studi e Ricerche in Neuroscienze Cognitive, Campus di Cesena, via Rasi e Spinelli, 176, 47521 Cesena, Italy
| | - Marta Bortoletto
- Laboratorio di Neurofisiologia, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, via pilastroni, 4, 25125 Brescia, Italy
| | - Giuseppe di Pellegrino
- Dipartimento di Psicologia, Alma Mater Studiorum – Università di Bologna, Centro Studi e Ricerche in Neuroscienze Cognitive, Campus di Cesena, via Rasi e Spinelli, 176, 47521 Cesena, Italy
| | - Vincenzo Romei
- Dipartimento di Psicologia, Alma Mater Studiorum – Università di Bologna, Centro Studi e Ricerche in Neuroscienze Cognitive, Campus di Cesena, via Rasi e Spinelli, 176, 47521 Cesena, Italy
- Facultad de Lenguas y Educación, Universidad Antonio de Nebrija, Madrid, 28015, Spain
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Chen C, Nibbio G, Kotozaki Y. Editorial: Methods and applications in psychopathology: new methods and trends for the understanding of neuropsychiatric disorders. Front Psychol 2023; 14:1242921. [PMID: 37546484 PMCID: PMC10400311 DOI: 10.3389/fpsyg.2023.1242921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 07/11/2023] [Indexed: 08/08/2023] Open
Affiliation(s)
- Chong Chen
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Ube, Japan
| | - Gabriele Nibbio
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Yuka Kotozaki
- Department of Hygiene and Preventive Medicine, School of Medicine, Iwate Medical University, Morioka, Japan
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19
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Bosl WJ, Bosquet Enlow M, Lock EF, Nelson CA. A biomarker discovery framework for childhood anxiety. Front Psychiatry 2023; 14:1158569. [PMID: 37533889 PMCID: PMC10393248 DOI: 10.3389/fpsyt.2023.1158569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 07/04/2023] [Indexed: 08/04/2023] Open
Abstract
Introduction Anxiety is the most common manifestation of psychopathology in youth, negatively affecting academic, social, and adaptive functioning and increasing risk for mental health problems into adulthood. Anxiety disorders are diagnosed only after clinical symptoms emerge, potentially missing opportunities to intervene during critical early prodromal periods. In this study, we used a new empirical approach to extracting nonlinear features of the electroencephalogram (EEG), with the goal of discovering differences in brain electrodynamics that distinguish children with anxiety disorders from healthy children. Additionally, we examined whether this approach could distinguish children with externalizing disorders from healthy children and children with anxiety. Methods We used a novel supervised tensor factorization method to extract latent factors from repeated multifrequency nonlinear EEG measures in a longitudinal sample of children assessed in infancy and at ages 3, 5, and 7 years of age. We first examined the validity of this method by showing that calendar age is highly correlated with latent EEG complexity factors (r = 0.77). We then computed latent factors separately for distinguishing children with anxiety disorders from healthy controls using a 5-fold cross validation scheme and similarly for distinguishing children with externalizing disorders from healthy controls. Results We found that latent factors derived from EEG recordings at age 7 years were required to distinguish children with an anxiety disorder from healthy controls; recordings from infancy, 3 years, or 5 years alone were insufficient. However, recordings from two (5, 7 years) or three (3, 5, 7 years) recordings gave much better results than 7 year recordings alone. Externalizing disorders could be detected using 3- and 5 years EEG data, also giving better results with two or three recordings than any single snapshot. Further, sex assigned at birth was an important covariate that improved accuracy for both disorder groups, and birthweight as a covariate modestly improved accuracy for externalizing disorders. Recordings from infant EEG did not contribute to the classification accuracy for either anxiety or externalizing disorders. Conclusion This study suggests that latent factors extracted from EEG recordings in childhood are promising candidate biomarkers for anxiety and for externalizing disorders if chosen at appropriate ages.
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Affiliation(s)
- William J. Bosl
- Center for AI & Medicine, University of San Francisco, San Francisco, CA, United States
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, United States
- Department of Pediatrics, Harvard Medical School, Boston, MA, United States
| | - Michelle Bosquet Enlow
- Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Eric F. Lock
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United States
| | - Charles A. Nelson
- Department of Pediatrics, Harvard Medical School, Boston, MA, United States
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Boston Children’s Hospital, Boston, MA, United States
- Harvard Graduate School of Education, Cambridge, MA, United States
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20
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Rouault M, Pereira I, Galioulline H, Fleming SM, Stephan KE, Manjaly ZM. Interoceptive and metacognitive facets of fatigue in multiple sclerosis. Eur J Neurosci 2023; 58:2603-2622. [PMID: 37208934 DOI: 10.1111/ejn.16048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 04/28/2023] [Accepted: 05/15/2023] [Indexed: 05/21/2023]
Abstract
Numerous disorders are characterised by fatigue as a highly disabling symptom. Fatigue plays a particularly important clinical role in multiple sclerosis (MS) where it exerts a profound impact on quality of life. Recent concepts of fatigue grounded in computational theories of brain-body interactions emphasise the role of interoception and metacognition in the pathogenesis of fatigue. So far, however, for MS, empirical data on interoception and metacognition are scarce. This study examined interoception and (exteroceptive) metacognition in a sample of 71 persons with a diagnosis of MS. Interoception was assessed by prespecified subscales of a standard questionnaire (Multidimensional Assessment of Interoceptive Awareness [MAIA]), while metacognition was investigated with computational models of choice and confidence data from a visual discrimination paradigm. Additionally, autonomic function was examined by several physiological measurements. Several hypotheses were tested based on a preregistered analysis plan. In brief, we found the predicted association of interoceptive awareness with fatigue (but not with exteroceptive metacognition) and an association of autonomic function with exteroceptive metacognition (but not with fatigue). Furthermore, machine learning (elastic net regression) showed that individual fatigue scores could be predicted out-of-sample from our measurements, with questionnaire-based measures of interoceptive awareness and sleep quality as key predictors. Our results support theoretical concepts of interoception as an important factor for fatigue and demonstrate the general feasibility of predicting individual levels of fatigue from simple questionnaire-based measures of interoception and sleep.
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Affiliation(s)
- Marion Rouault
- Institut du Cerveau et de la Moelle Épinière (ICM), Centre National de la Recherche Scientifique (CNRS), Hôpital Pitié Salpêtrière, Paris, France
- Département d'Études Cognitives, École Normale Supérieure, Université Paris Sciences et Lettres (PSL University), Paris, France
| | - Inês Pereira
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH, Zurich, Switzerland
| | - Herman Galioulline
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH, Zurich, Switzerland
| | - Stephen M Fleming
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Department of Experimental Psychology, University College London, London, UK
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
| | - Klaas Enno Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH, Zurich, Switzerland
- Max Planck Institute for Metabolism Research, Cologne, Germany
| | - Zina-Mary Manjaly
- Department of Neurology, Schulthess Clinic, Zurich, Switzerland
- Department of Health Sciences and Technology, ETH, Zurich, Switzerland
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21
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Manchia M, Murri MB. The role of pharmacogenomics in precision psychiatry. Pharmacogenomics 2023; 24:523-527. [PMID: 37458685 DOI: 10.2217/pgs-2023-0112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/22/2023] Open
Abstract
The field of psychiatry is facing an important paradigm shift in the provision of clinical care and mental health service organization toward personalization and integration of multimodal data science. This approach, termed precision psychiatry, aims at identifying subgroups of patients more prone to the development of a certain phenotype, such as symptoms or severe mental disorders (risk detection), and/or to guide treatment selection. Pharmacogenomics and computational psychiatry are two fundamental tools of precision psychiatry, which have seen increasing levels of integration in clinical settings. Here we present a brief overview of these two applications of precision psychiatry in clinical settings.
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Affiliation(s)
- Mirko Manchia
- Section of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, 09127, Italy
- Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, 09127,Italy
- Department of Pharmacology, Dalhousie University, Halifax, NS, B3H 4R2, Canada
| | - Martino Belvederi Murri
- Institute of Psychiatry, Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, 44121, Italy
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22
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Taylor S, Lavalley CA, Hakimi N, Stewart JL, Ironside M, Zheng H, White E, Guinjoan S, Paulus MP, Smith R. Active learning impairments in substance use disorders when resolving the explore-exploit dilemma: A replication and extension of previous computational modeling results. medRxiv 2023:2023.04.03.23288037. [PMID: 37066197 PMCID: PMC10104213 DOI: 10.1101/2023.04.03.23288037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Background Substance use disorders (SUDs) represent a major public health risk. Yet, our understanding of the mechanisms that maintain these disorders remains incomplete. In a recent computational modeling study, we found initial evidence that SUDs are associated with slower learning rates from negative outcomes and less value-sensitive choice (low "action precision"), which could help explain continued substance use despite harmful consequences. Methods Here we aimed to replicate and extend these results in a pre-registered study with a new sample of 168 individuals with SUDs and 99 healthy comparisons (HCs). We performed the same computational modeling and group comparisons as in our prior report (doi: 10.1016/j.drugalcdep.2020.108208) to confirm previously observed effects. After completing all pre-registered replication analyses, we then combined the previous and current datasets (N = 468) to assess whether differences were transdiagnostic or driven by specific disorders. Results Replicating prior results, SUDs showed slower learning rates for negative outcomes in both Bayesian and frequentist analyses (η 2 =.02). Previously observed differences in action precision were not confirmed. Logistic regressions including all computational parameters as predictors in the combined datasets could differentiate several specific disorders from HCs, but could not differentiate most disorders from each other. Conclusions These results provide robust evidence that individuals with SUDs have more difficulty adjusting behavior in the face of negative outcomes than HCs. They also suggest this effect is common across several different SUDs. Future research should examine its neural basis and whether learning rates could represent a new treatment target or moderator of treatment outcome.
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Affiliation(s)
- Samuel Taylor
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | | | - Navid Hakimi
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | | | | | - Haixia Zheng
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Evan White
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | | | | | - Ryan Smith
- Laureate Institute for Brain Research, Tulsa, OK, USA
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23
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Abstract
OBJECTIVES Predictive processing posits that perception emerges from inferential processes within a hierarchical cortical system. Alterations of these processes may result in psychotic experiences, such as hallucinations and delusions. Central to the predictive processing account of psychosis is the notion of aberrant weights attributed to prior information and sensory input. Based on the notion that previous perceptual choices represent a relevant source of prior information, we here asked whether the propensity towards psychotic experiences may be related to altered choice history biases in perceptual decision-making. METHODS We investigated the relationship between choice history biases in perceptual decision-making and psychosis proneness in the general population. Choice history biases and their adaptation to experimentally induced changes in stimulus serial dependencies were investigated in decision-making tasks with auditory (experiment 1) and visual (experiment 2) stimuli. We further explored a potential compensatory mechanism for reduced choice history biases by reliance on predictive cross-modal cues. RESULTS In line with our preregistered hypothesis, psychosis proneness was associated with decreased choice history biases in both experiments. This association is generalized across conditions with and without stimulus serial dependencies. We did not find consistent evidence for a compensatory reliance on cue information in psychosis-prone individuals across experiments. CONCLUSIONS Our results show reduced choice history biases in psychosis proneness. A compensatory mechanism between implicit choice history effects and explicit cue information is not supported unequivocally by our data.
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Affiliation(s)
- Anna-Lena Eckert
- Charité - Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, Charitéplatz 1, 10117 Berlin, Germany.,Bernstein Center for Computational Neuroscience Berlin, Unter den Linden 6, 10099 Berlin, Germany.,Department of Psychiatry and Neurosciences, Campus Mitte, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Yael Gounitski
- Department of Psychiatry and Neurosciences, Campus Mitte, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Matthias Guggenmos
- Department of Psychiatry and Neurosciences, Campus Mitte, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.,Health and Medical University, Institute for Mind, Brain and Behavior, Olympischer Weg 1, 14471 Potsdam, Germany
| | - Philipp Sterzer
- Bernstein Center for Computational Neuroscience Berlin, Unter den Linden 6, 10099 Berlin, Germany.,Department of Psychiatry and Neurosciences, Campus Mitte, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.,University of Basel, Department of Psychiatry (UPK), Wilhelm-Klein-Strasse 27, 4002 Basel, Switzerland
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24
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Soda T, Ahmadi A, Tani J, Honda M, Hanakawa T, Yamashita Y. Simulating developmental diversity: Impact of neural stochasticity on atypical flexibility and hierarchy. Front Psychiatry 2023; 14:1080668. [PMID: 37009124 PMCID: PMC10050443 DOI: 10.3389/fpsyt.2023.1080668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 02/21/2023] [Indexed: 03/17/2023] Open
Abstract
Introduction Investigating the pathological mechanisms of developmental disorders is a challenge because the symptoms are a result of complex and dynamic factors such as neural networks, cognitive behavior, environment, and developmental learning. Recently, computational methods have started to provide a unified framework for understanding developmental disorders, enabling us to describe the interactions among those multiple factors underlying symptoms. However, this approach is still limited because most studies to date have focused on cross-sectional task performance and lacked the perspectives of developmental learning. Here, we proposed a new research method for understanding the mechanisms of the acquisition and its failures in hierarchical Bayesian representations using a state-of-the-art computational model, referred to as in silico neurodevelopment framework for atypical representation learning. Methods Simple simulation experiments were conducted using the proposed framework to examine whether manipulating the neural stochasticity and noise levels in external environments during the learning process can lead to the altered acquisition of hierarchical Bayesian representation and reduced flexibility. Results Networks with normal neural stochasticity acquired hierarchical representations that reflected the underlying probabilistic structures in the environment, including higher-order representation, and exhibited good behavioral and cognitive flexibility. When the neural stochasticity was high during learning, top-down generation using higher-order representation became atypical, although the flexibility did not differ from that of the normal stochasticity settings. However, when the neural stochasticity was low in the learning process, the networks demonstrated reduced flexibility and altered hierarchical representation. Notably, this altered acquisition of higher-order representation and flexibility was ameliorated by increasing the level of noises in external stimuli. Discussion These results demonstrated that the proposed method assists in modeling developmental disorders by bridging between multiple factors, such as the inherent characteristics of neural dynamics, acquisitions of hierarchical representation, flexible behavior, and external environment.
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Affiliation(s)
- Takafumi Soda
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
- Department of NCNP Brain Physiology and Pathology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | | | - Jun Tani
- Cognitive Neurorobotics Research Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
| | - Manabu Honda
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Takashi Hanakawa
- Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yuichi Yamashita
- Department of Information Medicine, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
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25
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Rutherford S, Barkema P, Tso IF, Sripada C, Beckmann CF, Ruhe HG, Marquand AF. Evidence for embracing normative modeling. eLife 2023; 12:85082. [PMID: 36912775 PMCID: PMC10036120 DOI: 10.7554/elife.85082] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 03/10/2023] [Indexed: 03/14/2023] Open
Abstract
In this work, we expand the normative model repository introduced in Rutherford et al., 2022a to include normative models charting lifespan trajectories of structural surface area and brain functional connectivity, measured using two unique resting-state network atlases (Yeo-17 and Smith-10), and an updated online platform for transferring these models to new data sources. We showcase the value of these models with a head-to-head comparison between the features output by normative modeling and raw data features in several benchmarking tasks: mass univariate group difference testing (schizophrenia versus control), classification (schizophrenia versus control), and regression (predicting general cognitive ability). Across all benchmarks, we show the advantage of using normative modeling features, with the strongest statistically significant results demonstrated in the group difference testing and classification tasks. We intend for these accessible resources to facilitate the wider adoption of normative modeling across the neuroimaging community.
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Affiliation(s)
- Saige Rutherford
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
- Donders Institute, Radboud University Nijmegen, Nijmegen, Netherlands
- Department of Psychiatry, University of Michigan-Ann Arbor, Ann Arbor, United States
| | - Pieter Barkema
- Donders Institute, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Ivy F Tso
- Department of Psychiatry, University of Michigan-Ann Arbor, Ann Arbor, United States
- Department of Psychology, University of Michigan-Ann Arbor, Ann Arbor, United States
| | - Chandra Sripada
- Department of Psychiatry, University of Michigan-Ann Arbor, Ann Arbor, United States
- Department of Philosophy, University of Michigan-Ann Arbor, Ann Arbor, United States
| | - Christian F Beckmann
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
- Donders Institute, Radboud University Nijmegen, Nijmegen, Netherlands
- Center for Functional MRI of the Brain (FMRIB), Nuffield Department for Clinical Neuroscience, Welcome Centre for Integrative Neuroimaging, Oxford University, Oxford, United Kingdom
| | - Henricus G Ruhe
- Donders Institute, Radboud University Nijmegen, Nijmegen, Netherlands
- Department of Psychiatry, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
| | - Andre F Marquand
- Department of Cognitive Neuroscience, Radboud University Nijmegen Medical Centre, Nijmegen, Netherlands
- Donders Institute, Radboud University Nijmegen, Nijmegen, Netherlands
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Guath M, Willfors C, Björlin Avdic H, Nordgren A, Kleberg JL. Pupillary response in reward processing in adults with major depressive disorder in remission. J Int Neuropsychol Soc 2023; 29:306-15. [PMID: 35545874 DOI: 10.1017/S1355617722000224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Major depressive disorder (MDD) is associated with impaired reward processing and reward learning. The literature is inconclusive regarding whether these impairments persist after remission. The current study examined reward processing during a probabilistic learning task in individuals in remission from MDD (n = 19) and never depressed healthy controls (n = 31) matched for age and sex. The outcome measures were pupil dilation (an indirect index of noradrenergic activity and arousal) and computational modeling parameters. METHOD Participants completed two versions (facial/nonfacial feedback) of probabilistic reward learning task with changing contingencies. Pupil dilation was measured with a corneal reflection eye tracker. The hypotheses and analysis plan were preregistered. RESULT Healthy controls had larger pupil dilation following losses than gains (p <.001), whereas no significant difference between outcomes was found in individuals with a history of MDD, resulting in an interaction between group and outcome (β = 0.81, SE = 0.34, t = 2.37, p = .018). The rMDD group also achieved lower mean score at the last trial (t[46.77] = 2.12, p = .040) as well as a smaller proportion of correct choices (t[46.70] = 2.09, p = .041) compared with healthy controls. CONCLUSION Impaired reward processing may persist after remission from MDD and could constitute a latent risk factor for relapse. Measuring pupil dilation in a reward learning task is a promising method for identifying reward processing abnormalities linked to MDD. The task is simple and noninvasive, which makes it feasible for clinical research.
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Abstract
ABSTRACT This article briefly examines the life and work of the late clinical psychologist and philosopher of science Paul E. Meehl. His thesis in Clinical versus Statistical Prediction (1954) that the data combination performed by mechanical operations, as compared to clinicians, achieves higher accuracy in predicting human behavior is one of the earliest theoretical works that laid the groundwork for utilizing statistics and computational modeling in research in psychiatry and clinical psychology. For today's psychiatric researchers and clinicians grappling with the challenges of translating the ever-increasing data of the human mind into practice tools, Meehl's advocacy for both accurate modeling of the data and their clinically relevant use is timely.
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Ging-Jehli NR, Kraemer HC, Eugene Arnold L, Roley-Roberts ME, deBeus R. Cognitive markers for efficacy of neurofeedback for attention-deficit hyperactivity disorder - personalized medicine using computational psychiatry in a randomized clinical trial. J Clin Exp Neuropsychol 2023; 45:118-131. [PMID: 37157126 PMCID: PMC10515439 DOI: 10.1080/13803395.2023.2206637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 03/20/2023] [Accepted: 04/19/2023] [Indexed: 05/10/2023]
Abstract
BACKGROUND Exploring whether cognitive components (identified by baseline cognitive testing and computational modeling) moderate clinical outcome of neurofeedback (NF) for attention-deficit hyperactivity disorder (ADHD). METHOD 142 children (aged 7-10) with ADHD were randomly assigned to either NF (n = 84) or control treatment (n = 58) in a double-blind clinical trial (NCT02251743). The NF group received live, self-controlled downtraining of electroencephalographic theta/beta ratio power. The control group received identical-appearing reinforcement from prerecorded electroencephalograms from other children. 133 (78 NF, 55 control) children had cognitive processing measured at baseline with the Integrated Visual and Auditory Continuous Performance Test (IVA2-CPT) and were included in this analysis. A diffusion decision model applied to the IVA2-CPT data quantified two latent cognitive components deficient in ADHD: drift rate and drift bias, indexing efficiency and context sensitivity of cognitive processes involving information integration. We explored whether these cognitive components moderated the improvement in parent- and teacher-rated inattention symptoms from baseline to treatment end (primary clinical outcome). RESULTS Baseline cognitive components reflecting information integration (drift rate, drift bias) moderated the improvement in inattention due to NF vs. control treatment (p = 0.006). Specifically, those with either the most or least severe deficits in these components showed more improvement in parent- and teacher-rated inattention when assigned to NF (Cohen's d = 0.59) than when assigned to control (Cohen's d = -0.21). CONCLUSIONS Pre-treatment cognitive testing with computational modeling identified children who benefitted more from neurofeedback than control treatment for ADHD.
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Affiliation(s)
- Nadja R. Ging-Jehli
- Department of Psychology, The Ohio State University,
Columbus, OH 43210, USA; Department of Cognitive, Linguistic & Psychological
Sciences, Brown University, Providence, Rhode Island
| | - Helena C. Kraemer
- Department of Psychiatry and Behavioral Sciences, Stanford
University School of Medicine, Cupertino, CA 95014, USA
| | - L. Eugene Arnold
- Department of Psychiatry and Behavioral Health, The Ohio
State University; Nisonger Center UCEDD, Columbus, OH 43210, USA
| | | | - Roger deBeus
- Department of Psychology, University of North Carolina at
Asheville, Asheville, NC 28801, USA
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Pan Y, Wen Y, Wang Y, Schilbach L, Chen J. Interpersonal coordination in schizophrenia: a concise update on paradigms, computations, and neuroimaging findings. Psychoradiology 2023; 3:kkad002. [PMID: 38666124 PMCID: PMC10917372 DOI: 10.1093/psyrad/kkad002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 01/31/2023] [Accepted: 02/06/2023] [Indexed: 04/28/2024]
Affiliation(s)
- Yafeng Pan
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Yalan Wen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Yajie Wang
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Leonhard Schilbach
- Department of General Psychiatry 2 and Neuroimaging Section, LVR-Klinikum Düsseldorf, Düsseldorf 40629, Germany
- Medical Faculty, Ludwig-Maximilians University, Munich 80539, Germany
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang 322000, China
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Castro Martínez JC, Santamaría-García H. Understanding mental health through computers: An introduction to computational psychiatry. Front Psychiatry 2023; 14:1092471. [PMID: 36824671 PMCID: PMC9941647 DOI: 10.3389/fpsyt.2023.1092471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 01/16/2023] [Indexed: 02/10/2023] Open
Abstract
Computational psychiatry recently established itself as a new tool in the study of mental disorders and problems. Integration of different levels of analysis is creating computational phenotypes with clinical and research values, and constructing a way to arrive at precision psychiatry are part of this new branch. It conceptualizes the brain as a computational organ that receives from the environment parameters to respond to challenges through calculations and algorithms in continuous feedback and feedforward loops with a permanent degree of uncertainty. Through this conception, one can seize an understanding of the cerebral and mental processes in the form of theories or hypotheses based on data. Using these approximations, a better understanding of the disorder and its different determinant factors facilitates the diagnostics and treatment by having an individual, ecologic, and holistic approach. It is a tool that can be used to homologate and integrate multiple sources of information given by several theoretical models. In conclusion, it helps psychiatry achieve precision and reproducibility, which can help the mental health field achieve significant advancement. This article is a narrative review of the basis of the functioning of computational psychiatry with a critical analysis of its concepts.
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Affiliation(s)
- Juan Camilo Castro Martínez
- Departamento de Psiquiatría y Salud Mental, Facultad de Medicina, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Hernando Santamaría-García
- Ph.D. Programa de Neurociencias, Departamento de Psiquiatría y Salud Mental, Pontificia Universidad Javeriana, Bogotá, Colombia
- Centro de Memoria y Cognición Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia
- Global Brain Health Institute, University of California, San Francisco – Trinity College Dublin, San Francisco, CA, United States
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Gauld C, Depannemaecker D. Dynamical systems in computational psychiatry: A toy-model to apprehend the dynamics of psychiatric symptoms. Front Psychol 2023; 14:1099257. [PMID: 36844296 PMCID: PMC9945965 DOI: 10.3389/fpsyg.2023.1099257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 01/06/2023] [Indexed: 02/05/2023] Open
Abstract
Introduction These last years, scientific research focuses on the dynamical aspects of psychiatric disorders and their clinical significance. In this article, we proposed a theoretical framework formalized as a generic mathematical model capturing the heterogeneous individual evolutions of psychiatric symptoms. The first goal of this computational model based on differential equations is to illustrate the nonlinear dynamics of psychiatric symptoms. It offers an original approach to nonlinear dynamics to clinical psychiatrists. Methods In this study, we propose a 3+1 dimensions model (x, y, z + f) reproducing the clinical observations encountered in clinical psychiatry with: a variable modeling environmental noise (z) on the patient's internal factors (y) with its temporal specificities (f) and symptomatology (x). This toy-model is able to integrate empirical or simulated data from the influence of perceived environmental over time, their potential importance on the internal and subjective patient-specific elements, and their interaction with the apparent intensity of symptoms. Results Constrained by clinical observation of case formulations, the dynamics of psychiatric symptoms is studied through four main psychiatric conditions were modeled: i) a healthy situation, ii) a kind of psychiatric disorder evolving following an outbreak (i.e., schizophrenia spectrum), iii) a kind of psychiatric disorder evolving by kindling and bursts (e.g., bipolar and related disorders); iv) and a kind of psychiatric disorder evolving due to its high susceptibility to the environment (e.g., spersistent complex bereavement disorder). Moreover, we simulate the action of treatments on different psychiatric conditions. Discussion We show that the challenges of dynamical systems allow to understand the interactions of psychiatric symptoms with environmental, descriptive, subjective or biological variables. Although this non-linear dynamical model has limitations (e.g., explanatory scope or discriminant validity), simulations provide at least five main interests for clinical psychiatry, such as a visualization of the potential different evolution of psychiatric disorders, formulation of clinical cases, information about attracting states and bifurcations, or the possibility of a nosological refinement of psychiatric models (e.g., staging and symptom network models).
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Affiliation(s)
- Christophe Gauld
- Department of Child Psychiatry, University Hospital Lyon, Lyon, France,Institut des Sciences Cognitives Marc Jeannerod, UMR 5229 CNRS, Université Claude Bernard Lyon 1, Lyon, France,*Correspondence: Christophe Gauld ✉
| | - Damien Depannemaecker
- Centre National de la Recherche Scientifique (CNRS), Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Gif-sur-Yvette, France,Aix-Marseille University, INSERM, Institut de Neuroscience des Systèmes (INS), Marseille, France,Damien Depannemaecker ✉
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Moin Afshar N, Cinotti F, Martin D, Khamassi M, Calu DJ, Taylor JR, Groman SM. Reward-Mediated, Model-Free Reinforcement-Learning Mechanisms in Pavlovian and Instrumental Tasks Are Related. J Neurosci 2023; 43:458-471. [PMID: 36216504 PMCID: PMC9864557 DOI: 10.1523/jneurosci.1113-22.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 10/03/2022] [Accepted: 10/06/2022] [Indexed: 01/25/2023] Open
Abstract
Model-free and model-based computations are argued to distinctly update action values that guide decision-making processes. It is not known, however, if these model-free and model-based reinforcement learning mechanisms recruited in operationally based instrumental tasks parallel those engaged by pavlovian-based behavioral procedures. Recently, computational work has suggested that individual differences in the attribution of incentive salience to reward predictive cues, that is, sign- and goal-tracking behaviors, are also governed by variations in model-free and model-based value representations that guide behavior. Moreover, it is not appreciated if these systems that are characterized computationally using model-free and model-based algorithms are conserved across tasks for individual animals. In the current study, we used a within-subject design to assess sign-tracking and goal-tracking behaviors using a pavlovian conditioned approach task and then characterized behavior using an instrumental multistage decision-making (MSDM) task in male rats. We hypothesized that both pavlovian and instrumental learning processes may be driven by common reinforcement-learning mechanisms. Our data confirm that sign-tracking behavior was associated with greater reward-mediated, model-free reinforcement learning and that it was also linked to model-free reinforcement learning in the MSDM task. Computational analyses revealed that pavlovian model-free updating was correlated with model-free reinforcement learning in the MSDM task. These data provide key insights into the computational mechanisms mediating associative learning that could have important implications for normal and abnormal states.SIGNIFICANCE STATEMENT Model-free and model-based computations that guide instrumental decision-making processes may also be recruited in pavlovian-based behavioral procedures. Here, we used a within-subject design to test the hypothesis that both pavlovian and instrumental learning processes were driven by common reinforcement-learning mechanisms. Sign-tracking and goal-tracking behaviors were assessed in rats using a pavlovian conditioned approach task, and then instrumental behavior was characterized using an MSDM task. We report that sign-tracking behavior was associated with greater model-free, but not model-based, learning in the MSDM task. These data suggest that pavlovian and instrumental behaviors may be driven by conserved reinforcement-learning mechanisms.
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Affiliation(s)
- Neema Moin Afshar
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut 06511
| | - François Cinotti
- Department of Experimental Psychology, University of Oxford, Oxford OX2 6GG, United Kingdom
| | - David Martin
- Department of Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, Maryland 21201
| | - Mehdi Khamassi
- Institute of Intelligent Systems and Robotics, Centre National de la Recherche Scientifique, Sorbonne University, 75005 Paris, France
| | - Donna J Calu
- Department of Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, Maryland 21201
- Program in Neuroscience, University of Maryland School of Medicine, Baltimore, Maryland 21201
| | - Jane R Taylor
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut 06511
- Department of Psychology, Yale University, New Haven, Connecticut 06520
| | - Stephanie M Groman
- Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut 06511
- Department of Neuroscience, University of Minnesota Medical School, Minneapolis, Minnesota 55455
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota 55455
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Thome J, Pinger M, Durstewitz D, Sommer WH, Kirsch P, Koppe G. Model-based experimental manipulation of probabilistic behavior in interpretable behavioral latent variable models. Front Neurosci 2023; 16:1077735. [PMID: 36699538 PMCID: PMC9868576 DOI: 10.3389/fnins.2022.1077735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 12/14/2022] [Indexed: 01/11/2023] Open
Abstract
Introduction Interpretable latent variable models that probabilistically link behavioral observations to an underlying latent process have increasingly been used to draw inferences on cognition from observed behavior. The latent process usually connects experimental variables to cognitive computation. While such models provide important insights into the latent processes generating behavior, one important aspect has often been overlooked. They may also be used to generate precise and falsifiable behavioral predictions as a function of the modeled experimental variables. In doing so, they pinpoint how experimental conditions must be designed to elicit desired behavior and generate adaptive experiments. Methods These ideas are exemplified on the process of delay discounting (DD). After inferring DD models from behavior on a typical DD task, the models are leveraged to generate a second adaptive DD task. Experimental trials in this task are designed to elicit 9 graded behavioral discounting probabilities across participants. Models are then validated and contrasted to competing models in the field by assessing the ouf-of-sample prediction error. Results The proposed framework induces discounting probabilities on nine levels. In contrast to several alternative models, the applied model exhibits high validity as indicated by a comparably low prediction error. We also report evidence for inter-individual differences with respect to the most suitable models underlying behavior. Finally, we outline how to adapt the proposed method to the investigation of other cognitive processes including reinforcement learning. Discussion Inducing graded behavioral frequencies with the proposed framework may help to highly resolve the underlying cognitive construct and associated neuronal substrates.
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Affiliation(s)
- Janine Thome
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany,Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Mathieu Pinger
- Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Daniel Durstewitz
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany,Faculty of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
| | - Wolfgang H. Sommer
- Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Peter Kirsch
- Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany,Institute of Psychology, Heidelberg University, Heidelberg, Germany
| | - Georgia Koppe
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany,Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany,*Correspondence: Georgia Koppe,
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Pio-Lopez L, Kuchling F, Tung A, Pezzulo G, Levin M. Active inference, morphogenesis, and computational psychiatry. Front Comput Neurosci 2022; 16:988977. [PMID: 36507307 PMCID: PMC9731232 DOI: 10.3389/fncom.2022.988977] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 10/17/2022] [Indexed: 11/26/2022] Open
Abstract
Active inference is a leading theory in neuroscience that provides a simple and neuro-biologically plausible account of how action and perception are coupled in producing (Bayes) optimal behavior; and has been recently used to explain a variety of psychopathological conditions. In parallel, morphogenesis has been described as the behavior of a (non-neural) cellular collective intelligence solving problems in anatomical morphospace. In this article, we establish a link between the domains of cell biology and neuroscience, by analyzing disorders of morphogenesis as disorders of (active) inference. The aim of this article is three-fold. We want to: (i) reveal a connection between disorders of morphogenesis and disorders of active inference as apparent in psychopathological conditions; (ii) show how disorders of morphogenesis can be simulated using active inference; (iii) suggest that active inference can shed light on developmental defects or aberrant morphogenetic processes, seen as disorders of information processing, and perhaps suggesting novel intervention and repair strategies. We present four simulations illustrating application of these ideas to cellular behavior during morphogenesis. Three of the simulations show that the same forms of aberrant active inference (e.g., deficits of sensory attenuation and low sensory precision) that have been used to explain psychopathological conditions (e.g., schizophrenia and autism) also produce familiar disorders of development and morphogenesis when implemented at the level of the collective behavior of a group of cells. The fourth simulation involves two cells with too high precision, in which we show that the reduction of concentration signaling and sensitivity to the signals of other cells treats the development defect. Finally, we present the results of an experimental test of one of the model's predictions in early Xenopus laevis embryos: thioridazine (a dopamine antagonist that may reduce sensory precision in biological systems) induced developmental (anatomical) defects as predicted. The use of conceptual and empirical tools from neuroscience to understand the morphogenetic behavior of pre-neural agents offers the possibility of new approaches in regenerative medicine and evolutionary developmental biology.
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Affiliation(s)
- Léo Pio-Lopez
- Allen Discovery Center, Tufts University, Medford, MA, United States
| | - Franz Kuchling
- Allen Discovery Center, Tufts University, Medford, MA, United States
| | - Angela Tung
- Allen Discovery Center, Tufts University, Medford, MA, United States
| | - Giovanni Pezzulo
- Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
| | - Michael Levin
- Allen Discovery Center, Tufts University, Medford, MA, United States,Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, United States,*Correspondence: Michael Levin
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Abstract
In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.
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Affiliation(s)
- Zhe Sage Chen
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
- Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, NY 11201, USA
| | | | - Isaac R. Galatzer-Levy
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- Meta Reality Lab, New York, NY, USA
| | - Benedetta Bigio
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Carla Nasca
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
- The Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA 18015, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA
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36
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Nour MM, Liu Y, Dolan RJ. Functional neuroimaging in psychiatry and the case for failing better. Neuron 2022; 110:2524-2544. [PMID: 35981525 DOI: 10.1016/j.neuron.2022.07.005] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 06/06/2022] [Accepted: 07/08/2022] [Indexed: 12/27/2022]
Abstract
Psychiatric disorders encompass complex aberrations of cognition and affect and are among the most debilitating and poorly understood of any medical condition. Current treatments rely primarily on interventions that target brain function (drugs) or learning processes (psychotherapy). A mechanistic understanding of how these interventions mediate their therapeutic effects remains elusive. From the early 1990s, non-invasive functional neuroimaging, coupled with parallel developments in the cognitive neurosciences, seemed to signal a new era of neurobiologically grounded diagnosis and treatment in psychiatry. Yet, despite three decades of intense neuroimaging research, we still lack a neurobiological account for any psychiatric condition. Likewise, functional neuroimaging plays no role in clinical decision making. Here, we offer a critical commentary on this impasse and suggest how the field might fare better and deliver impactful neurobiological insights.
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Affiliation(s)
- Matthew M Nour
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK; Wellcome Trust Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK; Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK.
| | - Yunzhe Liu
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK; State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; Chinese Institute for Brain Research, Beijing 102206, China
| | - Raymond J Dolan
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK; Wellcome Trust Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK; State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
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37
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Rutherford S, Kia SM, Wolfers T, Fraza C, Zabihi M, Dinga R, Berthet P, Worker A, Verdi S, Ruhe HG, Beckmann CF, Marquand AF. The normative modeling framework for computational psychiatry. Nat Protoc 2022; 17:1711-1734. [PMID: 35650452 PMCID: PMC7613648 DOI: 10.1038/s41596-022-00696-5] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 03/17/2022] [Indexed: 11/09/2022]
Abstract
Normative modeling is an emerging and innovative framework for mapping individual differences at the level of a single subject or observation in relation to a reference model. It involves charting centiles of variation across a population in terms of mappings between biology and behavior, which can then be used to make statistical inferences at the level of the individual. The fields of computational psychiatry and clinical neuroscience have been slow to transition away from patient versus 'healthy' control analytic approaches, probably owing to a lack of tools designed to properly model biological heterogeneity of mental disorders. Normative modeling provides a solution to address this issue and moves analysis away from case-control comparisons that rely on potentially noisy clinical labels. Here we define a standardized protocol to guide users through, from start to finish, normative modeling analysis using the Predictive Clinical Neuroscience toolkit (PCNtoolkit). We describe the input data selection process, provide intuition behind the various modeling choices and conclude by demonstrating several examples of downstream analyses that the normative model may facilitate, such as stratification of high-risk individuals, subtyping and behavioral predictive modeling. The protocol takes ~1-3 h to complete.
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Affiliation(s)
- Saige Rutherford
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands.
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands.
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA.
| | - Seyed Mostafa Kia
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Psychiatry, Utrecht University Medical Center, Utrecht, the Netherlands
| | - Thomas Wolfers
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research, University of Oslo, Oslo, Norway
| | - Charlotte Fraza
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Mariam Zabihi
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Richard Dinga
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Pierre Berthet
- Department of Psychology, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research, University of Oslo, Oslo, Norway
| | - Amanda Worker
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Serena Verdi
- Centre for Medical Image Computing, Medical Physics and Biomedical Engineering, University College London, London, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Henricus G Ruhe
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Christian F Beckmann
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
- Centre for Functional MRI of the Brain, University of Oxford, Oxford, UK
| | - Andre F Marquand
- Donders Institute for Brain, Cognition, and Behavior, Radboud University, Nijmegen, the Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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Thome J, Pinger M, Halli P, Durstewitz D, Sommer WH, Kirsch P, Koppe G. A Model Guided Approach to Evoke Homogeneous Behavior During Temporal Reward and Loss Discounting. Front Psychiatry 2022; 13:846119. [PMID: 35800024 PMCID: PMC9253427 DOI: 10.3389/fpsyt.2022.846119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 05/18/2022] [Indexed: 12/14/2022] Open
Abstract
Background The tendency to devaluate future options as a function of time, known as delay discounting, is associated with various factors such as psychiatric illness and personality. Under identical experimental conditions, individuals may therefore strongly differ in the degree to which they discount future options. In delay discounting tasks, this inter-individual variability inevitably results in an unequal number of discounted trials per subject, generating difficulties in linking delay discounting to psychophysiological and neural correlates. Many studies have therefore focused on assessing delay discounting adaptively. Here, we extend these approaches by developing an adaptive paradigm which aims at inducing more comparable and homogeneous discounting frequencies across participants on a dimensional scale. Method The proposed approach probabilistically links a (common) discounting function to behavior to obtain a probabilistic model, and then exploits the model to obtain a formal condition which defines how to construe experimental trials so as to induce any desired discounting probability. We first infer subject-level models on behavior on a non-adaptive delay discounting task and then use these models to generate adaptive trials designed to evoke graded relative discounting frequencies of 0.3, 0.5, and 0.7 in each participant. We further compare and evaluate common models in the field through out-of-sample prediction error estimates, to iteratively improve the trial-generating model and paradigm. Results The developed paradigm successfully increases discounting behavior during both reward and loss discounting. Moreover, it evokes graded relative choice frequencies in line with model-based expectations (i.e., 0.3, 0.5, and 0.7) suggesting that we can successfully homogenize behavior. Our model comparison analyses indicate that hyperboloid models are superior in predicting unseen discounting behavior to more conventional hyperbolic and exponential models. We report out-of-sample error estimates as well as commonalities and differences between reward and loss discounting, demonstrating for instance lower discounting rates, as well as differences in delay perception in loss discounting. Conclusion The present work proposes a model-based framework to evoke graded responses linked to cognitive function at a single subject level. Such a framework may be used in the future to measure cognitive functions on a dimensional rather than dichotomous scale.
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Affiliation(s)
- Janine Thome
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Mathieu Pinger
- Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Patrick Halli
- Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Daniel Durstewitz
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Wolfgang H. Sommer
- Institute for Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Peter Kirsch
- Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Institute of Psychology, Heidelberg University, Heidelberg, Germany
| | - Georgia Koppe
- Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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Leptourgos P, Bansal S, Dutterer J, Culbreth A, Powers A, Suthaharan P, Kenney J, Erickson M, Waltz J, Wijtenburg SA, Gaston F, Rowland LM, Gold J, Corlett P. Relating Glutamate, Conditioned, and Clinical Hallucinations via 1H-MR Spectroscopy. Schizophr Bull 2022; 48:912-920. [PMID: 35199836 PMCID: PMC9212089 DOI: 10.1093/schbul/sbac006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
BACKGROUND AND HYPOTHESIS Hallucinations may be driven by an excessive influence of prior expectations on current experience. Initial work has supported that contention and implicated the anterior insula in the weighting of prior beliefs. STUDY DESIGN Here we induce hallucinated tones by associating tones with the presentation of a visual cue. We find that people with schizophrenia who hear voices are more prone to the effect and using computational modeling we show they overweight their prior beliefs. In the same participants, we also measured glutamate levels in anterior insula, anterior cingulate, dorsolateral prefrontal, and auditory cortices, using magnetic resonance spectroscopy. STUDY RESULTS We found a negative relationship between prior-overweighting and glutamate levels in the insula that was not present for any of the other voxels or parameters. CONCLUSIONS Through computational psychiatry, we bridge a pathophysiological theory of psychosis (glutamate hypofunction) with a cognitive model of hallucinations (prior-overweighting) with implications for the development of new treatments for hallucinations.
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Affiliation(s)
- Pantelis Leptourgos
- Department of Psychiatry, Connecticut Mental Health Center, Yale University, New Haven, CT,USA
| | - Sonia Bansal
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Catonsville, MD,USA
| | - Jenna Dutterer
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Catonsville, MD,USA
| | - Adam Culbreth
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Catonsville, MD,USA
| | - Albert Powers
- Department of Psychiatry, Connecticut Mental Health Center, Yale University, New Haven, CT,USA
| | - Praveen Suthaharan
- Department of Psychiatry, Connecticut Mental Health Center, Yale University, New Haven, CT,USA
| | - Joshua Kenney
- Department of Psychiatry, Connecticut Mental Health Center, Yale University, New Haven, CT,USA
| | - Molly Erickson
- Department of Psychiatry, University of Chicago, Chicago, IL,USA
| | - James Waltz
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Catonsville, MD,USA
| | - S Andrea Wijtenburg
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Catonsville, MD,USA
| | - Frank Gaston
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Catonsville, MD,USA
| | - Laura M Rowland
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Catonsville, MD,USA
| | - James Gold
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Catonsville, MD,USA
| | - Philip Corlett
- To whom correspondence should be addressed; 34 Park Street, New Haven, CT 06511, USA; tel: 203-974-7866, fax: 203 974 7866, e-mail:
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40
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Le Heron CJ. Reward and actions and the game of life. J Neurol Neurosurg Psychiatry 2022; 93:458. [PMID: 35078914 DOI: 10.1136/jnnp-2021-328302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 12/22/2021] [Indexed: 11/03/2022]
Affiliation(s)
- Campbell J Le Heron
- Department of Medicine, University of Otago, Christchurch, New Zealand .,New Zealand Brain Research Institute, Christchurch, Canterbury, New Zealand.,Department of Neurology, Canterbury District Health Board, Christchurch, New Zealand
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41
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Constant A, Badcock P, Friston K, Kirmayer LJ. Integrating Evolutionary, Cultural, and Computational Psychiatry: A Multilevel Systemic Approach. Front Psychiatry 2022; 13:763380. [PMID: 35444580 PMCID: PMC9013887 DOI: 10.3389/fpsyt.2022.763380] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 02/14/2022] [Indexed: 12/21/2022] Open
Abstract
This paper proposes an integrative perspective on evolutionary, cultural and computational approaches to psychiatry. These three approaches attempt to frame mental disorders as multiscale entities and offer modes of explanations and modeling strategies that can inform clinical practice. Although each of these perspectives involves systemic thinking, each is limited in its ability to address the complex developmental trajectories and larger social systemic interactions that lead to mental disorders. Inspired by computational modeling in theoretical biology, this paper aims to integrate the modes of explanation offered by evolutionary, cultural and computational psychiatry in a multilevel systemic perspective. We apply the resulting Evolutionary, Cultural and Computational (ECC) model to Major Depressive Disorder (MDD) to illustrate how this integrative approach can guide research and practice in psychiatry.
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Affiliation(s)
- Axel Constant
- Department of Philosophy, The University of Sydney, Darlington, NSW, Australia
| | - Paul Badcock
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
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Abstract
Depressive and anxiety disorders often comorbid, which causes more severe impairments. The high comorbidity and shared genetic and psychological factors between the 2 disorders have brought arguments about whether they represent a common construct, and whether the current classification is meaningful. In this editorial, a state-of-the-art overview of recent studies on the underlying mechanism of such comorbidity, and the association between and differentiation of the 2 disorders is provided. Recent studies employing data-driven approaches such as latent class analysis (LCA) and network analysis to investigate the symptomatology of depression and anxiety have indicated unique characteristics and bridging symptoms of their comorbidity. Whereas previous neurobiological and neuroendocrinological studies reported common alterations in prefrontal-limbic pathways, serotonergic projections and the hypothalamic-pituitary-adrenal (HPA) axis, recent research suggests that distinct neural circuits and heterogeneous changes in HPA activity may exist in depression when compared to anxiety. Lastly, both depression and anxiety have been long associated with decision-making deficits; however, emerging evidence from computational psychiatry demonstrate that there may be unique neurocognitive and computational alterations in each disorder. By investigating the common and unique symptomatic characteristics and underlying neurobiological and neurocomputational mechanisms of the 2 disorders as well as their comorbidity, it can be concluded that recent studies have greatly advanced our understanding of the etiology and neuropathophysiology of these disorders.
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Affiliation(s)
- Chong Chen
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Japan
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43
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Abstract
The National Institute of Mental Health (NIMH) addressed in its 2008 Strategic Plan an emerging concern that the current diagnostic system was hampering translational research, as accumulating data suggested that disorder categories constituted heterogeneous syndromes rather than specific diseases. However, established practices in peer review placed high priority on extant disorders in evaluating grant applications for mental illness. To provide guidelines for alternative study designs, NIMH included a goal to develop new ways of studying psychopathology based on dimensions of measurable behavior and related neurobiological measures. The Research Domain Criteria (RDoC) project is the result, intended to build a literature that informs new conceptions of mental illness and future revisions to diagnostic manuals. The framework calls for the study of empirically-derived fundamental dimensions as characterized by related behavioral/psychological and neurobiological data (e.g., reward valuation, working memory). RDoC also emphasizes full-range dimensional approaches (from typical to increasingly abnormal), neurodevelopment and environmental effects, and research designs that integrate data across behavioral, biological, and self-report measures. This commentary provides an overview of the project's first decade and its potential future directions. RDoC remains grounded in experimental psychopathology perspectives, and its progress is strongly linked to psychological measurement and integrative approaches to brain-behavior relationships.
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Hagiwara K, Mochizuki Y, Chen C, Lei H, Hirotsu M, Matsubara T, Nakagawa S. Nonlinear Probability Weighting in Depression and Anxiety: Insights From Healthy Young Adults. Front Psychiatry 2022; 13:810867. [PMID: 35401267 PMCID: PMC8988187 DOI: 10.3389/fpsyt.2022.810867] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 02/17/2022] [Indexed: 11/13/2022] Open
Abstract
Both depressive and anxiety disorders have been associated with excessive risk avoidant behaviors, which are considered an important contributor to the maintenance and recurrence of these disorders. However, given the high comorbidity between the two disorders, their independent association with risk preference remains unclear. Furthermore, due to the involvement of multiple cognitive computational factors in the decision-making tasks employed so far, the precise underlying mechanisms of risk preference are unknown. In the present study, we set out to investigate the common versus unique cognitive computational mechanisms of risk preference in depression and anxiety using a reward-based decision-making task and computational modeling based on economic theories. Specifically, in model-based analysis, we decomposed risk preference into utility sensitivity (a power function) and probability weighting (the one-parameter Prelec weighting function). Multiple linear regression incorporating depression (BDI-II) and anxiety (STAI state anxiety) simultaneously indicated that only depression was associated with one such risk preference parameter, probability weighting. As the symptoms of depression increased, subjects' tendency to overweight small probabilities and underweight large probabilities decreased. Neither depression nor anxiety was associated with utility sensitivity. These associations remained even after controlling covariates or excluding anxiety-relevant items from the depression scale. To our knowledge, this is the first study to assess risk preference due to a concave utility function and nonlinear probability weighting separately for depression and anxiety using computational modeling. Our results provide a mechanistic account of risk avoidance and may improve our understanding of decision-making deficits in depression and anxiety.
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Affiliation(s)
- Kosuke Hagiwara
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Ube, Japan
| | | | - Chong Chen
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Ube, Japan
| | - Huijie Lei
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Ube, Japan
| | - Masako Hirotsu
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Ube, Japan
| | - Toshio Matsubara
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Ube, Japan
| | - Shin Nakagawa
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Ube, Japan
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Abstract
Disruptive innovation is a cornerstone of various disciplines, particularly in the business world, where paradigm‐altering approaches are often lauded. As a construct, disruptive psychiatry can be considered to embody such an approach by the pursuit of innovations within the field which test boundaries and shake up the status quo. Given the importance of addressing the current mental health pandemic, and the deficit of highly effective pharmacological treatments for various conditions, innovative disruptive thinking is required. Significant promise exists via the provision of potentially more effective innovative therapeutic options, including digital interventions, psychedelic medicines, microbiome‐altering treatments, brain stimulation technology, and nanotechnology‐based delivery systems. These approaches may be further advanced by individualization of the treatments using computational technology (including quantum computing, machine learning, and artificial intelligence) and genomics.
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Affiliation(s)
- Jerome Sarris
- NICM Health Research Institute, Western Sydney University, Westmead, New South Wales, Australia.,The Florey Institute of Neuroscience and Mental Health & Department of Psychiatry, Melbourne University, Melbourne, Victoria, Australia.,Psychae Institute, Melbourne, Victoria, Australia
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46
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Ging-Jehli NR, Arnold LE, Roley-Roberts ME, deBeus R. Characterizing Underlying Cognitive Components of ADHD Presentations and Co-morbid Diagnoses: A Diffusion Decision Model Analysis. J Atten Disord 2022; 26:706-722. [PMID: 34085557 DOI: 10.1177/10870547211020087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
OBJECTIVE To Explore whether subtypes and comorbidities of attention-deficit hyperactivity disorder (ADHD) induce distinct biases in cognitive components involved in information processing. METHOD Performance on the Integrated Visual and Auditory Continuous Performance Test (IVA-CPT) was compared between 150 children (aged 7 to 10) with ADHD, grouped by DSM-5 presentation (ADHD-C, ADHD-I) or co-morbid diagnoses (anxiety, oppositional defiant disorder [ODD], both, neither), and 60 children without ADHD. Diffusion decision modeling decomposed performance into cognitive components. RESULTS Children with ADHD had poorer information integration than controls. Children with ADHD-C were more sensitive to changes in presentation modality (auditory/visual) than those with ADHD-I and controls. Above and beyond these results, children with ADHD+anxiety+ODD had larger increases in response biases when targets became frequent than children with ADHD-only or with ADHD and one comorbidity. CONCLUSION ADHD presentations and comorbidities have distinct cognitive characteristics quantifiable using DDM and IVA-CPT. We discuss implications for tailored cognitive-behavioral therapy.
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Redish AD, Kepecs A, Anderson LM, Calvin OL, Grissom NM, Haynos AF, Heilbronner SR, Herman AB, Jacob S, Ma S, Vilares I, Vinogradov S, Walters CJ, Widge AS, Zick JL, Zilverstand A. Computational validity: using computation to translate behaviours across species. Philos Trans R Soc Lond B Biol Sci 2022; 377:20200525. [PMID: 34957854 PMCID: PMC8710889 DOI: 10.1098/rstb.2020.0525] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 07/28/2021] [Indexed: 11/12/2022] Open
Abstract
We propose a new conceptual framework (computational validity) for translation across species and populations based on the computational similarity between the information processing underlying parallel tasks. Translating between species depends not on the superficial similarity of the tasks presented, but rather on the computational similarity of the strategies and mechanisms that underlie those behaviours. Computational validity goes beyond construct validity by directly addressing questions of information processing. Computational validity interacts with circuit validity as computation depends on circuits, but similar computations could be accomplished by different circuits. Because different individuals may use different computations to accomplish a given task, computational validity suggests that behaviour should be understood through the subject's point of view; thus, behaviour should be characterized on an individual level rather than a task level. Tasks can constrain the computational algorithms available to a subject and the observed subtleties of that behaviour can provide information about the computations used by each individual. Computational validity has especially high relevance for the study of psychiatric disorders, given the new views of psychiatry as identifying and mediating information processing dysfunctions that may show high inter-individual variability, as well as for animal models investigating aspects of human psychiatric disorders. This article is part of the theme issue 'Systems neuroscience through the lens of evolutionary theory'.
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Affiliation(s)
- A. David Redish
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Adam Kepecs
- Department of Neuroscience, Washington University in St. Louis, St Louis, MO 63110, USA
- Department of Psychiatry, Washington University in St. Louis, St Louis, MO 63110, USA
| | - Lisa M. Anderson
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
| | - Olivia L. Calvin
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
| | - Nicola M. Grissom
- Department of Psychology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Ann F. Haynos
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
| | | | - Alexander B. Herman
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
| | - Suma Jacob
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
| | - Sisi Ma
- Department of Medicine - Institute for Health Informatics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Iris Vilares
- Department of Psychology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Sophia Vinogradov
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
| | - Cody J. Walters
- Graduate Program in Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Alik S. Widge
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
| | - Jennifer L. Zick
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
| | - Anna Zilverstand
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
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Katthagen T, Fromm S, Wieland L, Schlagenhauf F. Models of Dynamic Belief Updating in Psychosis-A Review Across Different Computational Approaches. Front Psychiatry 2022; 13:814111. [PMID: 35492702 PMCID: PMC9039658 DOI: 10.3389/fpsyt.2022.814111] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 02/18/2022] [Indexed: 11/20/2022] Open
Abstract
To understand the dysfunctional mechanisms underlying maladaptive reasoning of psychosis, computational models of decision making have widely been applied over the past decade. Thereby, a particular focus has been on the degree to which beliefs are updated based on new evidence, expressed by the learning rate in computational models. Higher order beliefs about the stability of the environment can determine the attribution of meaningfulness to events that deviate from existing beliefs by interpreting these either as noise or as true systematic changes (volatility). Both, the inappropriate downplaying of important changes as noise (belief update too low) as well as the overly flexible adaptation to random events (belief update too high) were theoretically and empirically linked to symptoms of psychosis. Whereas models with fixed learning rates fail to adjust learning in reaction to dynamic changes, increasingly complex learning models have been adopted in samples with clinical and subclinical psychosis lately. These ranged from advanced reinforcement learning models, over fully Bayesian belief updating models to approximations of fully Bayesian models with hierarchical learning or change point detection algorithms. It remains difficult to draw comparisons across findings of learning alterations in psychosis modeled by different approaches e.g., the Hierarchical Gaussian Filter and change point detection. Therefore, this review aims to summarize and compare computational definitions and findings of dynamic belief updating without perceptual ambiguity in (sub)clinical psychosis across these different mathematical approaches. There was strong heterogeneity in tasks and samples. Overall, individuals with schizophrenia and delusion-proneness showed lower behavioral performance linked to failed differentiation between uninformative noise and environmental change. This was indicated by increased belief updating and an overestimation of volatility, which was associated with cognitive deficits. Correlational evidence for computational mechanisms and positive symptoms is still sparse and might diverge from the group finding of instable beliefs. Based on the reviewed studies, we highlight some aspects to be considered to advance the field with regard to task design, modeling approach, and inclusion of participants across the psychosis spectrum. Taken together, our review shows that computational psychiatry offers powerful tools to advance our mechanistic insights into the cognitive anatomy of psychotic experiences.
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Affiliation(s)
- Teresa Katthagen
- Department of Psychiatry and Neurosciences, CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Sophie Fromm
- Department of Psychiatry and Neurosciences, CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Einstein Center for Neurosciences, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Lara Wieland
- Department of Psychiatry and Neurosciences, CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Einstein Center for Neurosciences, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Florian Schlagenhauf
- Department of Psychiatry and Neurosciences, CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Einstein Center for Neurosciences, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.,Bernstein Center for Computational Neuroscience, Berlin, Germany.,NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
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Chase HW, Wilson RC, Waltz JA. Editorial: Computational accounts of reinforcement learning and decision making in psychiatric disorders. Front Psychiatry 2022; 13:966369. [PMID: 35958661 PMCID: PMC9358282 DOI: 10.3389/fpsyt.2022.966369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 07/05/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Henry W Chase
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Robert C Wilson
- Department of Psychology, University of Arizona, Tucson, AZ, United States
| | - James A Waltz
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, United States
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Ribba B. Reinforcement learning as an innovative model-based approach: Examples from precision dosing, digital health and computational psychiatry. Front Pharmacol 2022; 13:1094281. [PMID: 36873047 PMCID: PMC9981647 DOI: 10.3389/fphar.2022.1094281] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 12/12/2022] [Indexed: 02/19/2023] Open
Abstract
Model-based approaches are instrumental for successful drug development and use. Anchored within pharmacological principles, through mathematical modeling they contribute to the quantification of drug response variability and enables precision dosing. Reinforcement learning (RL)-a set of computational methods addressing optimization problems as a continuous learning process-shows relevance for precision dosing with high flexibility for dosing rule adaptation and for coping with high dimensional efficacy and/or safety markers, constituting a relevant approach to take advantage of data from digital health technologies. RL can also support contributions to the successful development of digital health applications, recognized as key players of the future healthcare systems, in particular for reducing the burden of non-communicable diseases to society. RL is also pivotal in computational psychiatry-a way to characterize mental dysfunctions in terms of aberrant brain computations-and represents an innovative modeling approach forpsychiatric indications such as depression or substance abuse disorders for which digital therapeutics are foreseen as promising modalities.
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Affiliation(s)
- Benjamin Ribba
- Roche Pharma Research and Early Development (pRED), F. Hoffmann-La Roche Ltd, Basel, Switzerland
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