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Ging-Jehli NR, Kuhn M, Blank JM, Chanthrakumar P, Steinberger DC, Yu Z, Herrington TM, Dillon DG, Pizzagalli DA, Frank MJ. Cognitive Signatures of Depressive and Anhedonic Symptoms and Affective States Using Computational Modeling and Neurocognitive Testing. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00056-9. [PMID: 38401881 DOI: 10.1016/j.bpsc.2024.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 02/03/2024] [Accepted: 02/09/2024] [Indexed: 02/26/2024]
Abstract
BACKGROUND Deeper phenotyping may improve our understanding of depression. Because depression is heterogeneous, extracting cognitive signatures associated with severity of depressive symptoms, anhedonia, and affective states is a promising approach. METHODS Sequential sampling models decomposed behavior from an adaptive approach-avoidance conflict task into computational parameters quantifying latent cognitive signatures. Fifty unselected participants completed clinical scales and the approach-avoidance conflict task by either approaching or avoiding trials offering monetary rewards and electric shocks. RESULTS Decision dynamics were best captured by a sequential sampling model with linear collapsing boundaries varying by net offer values, and with drift rates varying by trial-specific reward and aversion, reflecting net evidence accumulation toward approach or avoidance. Unlike conventional behavioral measures, these computational parameters revealed distinct associations with self-reported symptoms. Specifically, passive avoidance tendencies, indexed by starting point biases, were associated with greater severity of depressive symptoms (R = 0.34, p = .019) and anhedonia (R = 0.49, p = .001). Depressive symptoms were also associated with slower encoding and response execution, indexed by nondecision time (R = 0.37, p = .011). Higher reward sensitivity for offers with negative net values, indexed by drift rates, was linked to more sadness (R = 0.29, p = .042) and lower positive affect (R = -0.33, p = .022). Conversely, higher aversion sensitivity was associated with more tension (R = 0.33, p = .025). Finally, less cautious response patterns, indexed by boundary separation, were linked to more negative affect (R = -0.40, p = .005). CONCLUSIONS We demonstrated the utility of multidimensional computational phenotyping, which could be applied to clinical samples to improve characterization and treatment selection.
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Affiliation(s)
- Nadja R Ging-Jehli
- Carney Institute for Brain Science, Department of Cognitive, Linguistic, & Psychological Sciences, Brown University, Providence, Rhode Island.
| | - Manuel Kuhn
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Jacob M Blank
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts
| | - Pranavan Chanthrakumar
- Carney Institute for Brain Science, Department of Cognitive, Linguistic, & Psychological Sciences, Brown University, Providence, Rhode Island; Warren Alpert Medical School of Brown University, Providence, Rhode Island
| | - David C Steinberger
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts
| | - Zeyang Yu
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Todd M Herrington
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Daniel G Dillon
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Diego A Pizzagalli
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Michael J Frank
- Carney Institute for Brain Science, Department of Cognitive, Linguistic, & Psychological Sciences, Brown University, Providence, Rhode Island
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2
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Hodson R, Mehta M, Smith R. The empirical status of predictive coding and active inference. Neurosci Biobehav Rev 2024; 157:105473. [PMID: 38030100 DOI: 10.1016/j.neubiorev.2023.105473] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/27/2023] [Accepted: 11/16/2023] [Indexed: 12/01/2023]
Abstract
Research on predictive processing models has focused largely on two specific algorithmic theories: Predictive Coding for perception and Active Inference for decision-making. While these interconnected theories possess broad explanatory potential, they have only recently begun to receive direct empirical evaluation. Here, we review recent studies of Predictive Coding and Active Inference with a focus on evaluating the degree to which they are empirically supported. For Predictive Coding, we find that existing empirical evidence offers modest support. However, some positive results can also be explained by alternative feedforward (e.g., feature detection-based) models. For Active Inference, most empirical studies have focused on fitting these models to behavior as a means of identifying and explaining individual or group differences. While Active Inference models tend to explain behavioral data reasonably well, there has not been a focus on testing empirical validity of active inference theory per se, which would require formal comparison to other models (e.g., non-Bayesian or model-free reinforcement learning models). This review suggests that, while promising, a number of specific research directions are still necessary to evaluate the empirical adequacy and explanatory power of these algorithms.
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Affiliation(s)
| | | | - Ryan Smith
- Laureate Institute for Brain Research, USA.
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3
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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. Drug Alcohol Depend 2023; 252:110945. [PMID: 37717307 PMCID: PMC10635739 DOI: 10.1016/j.drugalcdep.2023.110945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 08/15/2023] [Accepted: 08/18/2023] [Indexed: 09/19/2023]
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 (partial η2=.02). Previously observed differences in action precision were not confirmed. Learning rates for positive outcomes were also similar between groups. 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 adjust behavior more slowly 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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4
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Sprevak M, Smith R. An Introduction to Predictive Processing Models of Perception and Decision-Making. Top Cogn Sci 2023. [PMID: 37899002 DOI: 10.1111/tops.12704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 08/30/2023] [Accepted: 10/06/2023] [Indexed: 10/31/2023]
Abstract
The predictive processing framework includes a broad set of ideas, which might be articulated and developed in a variety of ways, concerning how the brain may leverage predictive models when implementing perception, cognition, decision-making, and motor control. This article provides an up-to-date introduction to the two most influential theories within this framework: predictive coding and active inference. The first half of the paper (Sections 2-5) reviews the evolution of predictive coding, from early ideas about efficient coding in the visual system to a more general model encompassing perception, cognition, and motor control. The theory is characterized in terms of the claims it makes at Marr's computational, algorithmic, and implementation levels of description, and the conceptual and mathematical connections between predictive coding, Bayesian inference, and variational free energy (a quantity jointly evaluating model accuracy and complexity) are explored. The second half of the paper (Sections 6-8) turns to recent theories of active inference. Like predictive coding, active inference models assume that perceptual and learning processes minimize variational free energy as a means of approximating Bayesian inference in a biologically plausible manner. However, these models focus primarily on planning and decision-making processes that predictive coding models were not developed to address. Under active inference, an agent evaluates potential plans (action sequences) based on their expected free energy (a quantity that combines anticipated reward and information gain). The agent is assumed to represent the world as a partially observable Markov decision process with discrete time and discrete states. Current research applications of active inference models are described, including a range of simulation work, as well as studies fitting models to empirical data. The paper concludes by considering future research directions that will be important for further development of both models.
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Affiliation(s)
- Mark Sprevak
- School of Philosophy, Psychology and Language Sciences, University of Edinburgh
| | - Ryan Smith
- Laureate Institute for Brain Research, Tulsa, Oklahoma
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5
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Enkhtaivan E, Nishimura J, Cochran A. Placing Approach-Avoidance Conflict Within the Framework of Multi-objective Reinforcement Learning. Bull Math Biol 2023; 85:116. [PMID: 37837562 DOI: 10.1007/s11538-023-01216-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 09/20/2023] [Indexed: 10/16/2023]
Abstract
Many psychiatric disorders are marked by impaired decision-making during an approach-avoidance conflict. Current experiments elicit approach-avoidance conflicts in bandit tasks by pairing an individual's actions with consequences that are simultaneously desirable (reward) and undesirable (harm). We frame approach-avoidance conflict tasks as a multi-objective multi-armed bandit. By defining a general decision-maker as a limiting sequence of actions, we disentangle the decision process from learning. Each decision maker can then be identified as a multi-dimensional point representing its long-term average expected outcomes, while different decision making models can be associated by the geometry of their 'feasible region', the set of all possible long term performances on a fixed task. We introduce three example decision-makers based on popular reinforcement learning models and characterize their feasible regions, including whether they can be Pareto optimal. From this perspective, we find that existing tasks are unable to distinguish between the three examples of decision-makers. We show how to design new tasks whose geometric structure can be used to better distinguish between decision-makers. These findings are expected to guide the design of approach-avoidance conflict tasks and the modeling of resulting decision-making behavior.
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Affiliation(s)
- Enkhzaya Enkhtaivan
- Department of Mathematics, University of Wisconsin, 480 Lincoln Drive, Madison, 53706, WI, USA
| | - Joel Nishimura
- School of Mathematical and Natural Sciences, Arizona State University, PO Box 37100, Phoenix, 85069, AZ, USA
| | - Amy Cochran
- Department of Mathematics, University of Wisconsin, 480 Lincoln Drive, Madison, 53706, WI, USA.
- Department of Population Health Sciences, University of Wisconsin, 610 Walnut Street, Madison, 53726, WI, USA.
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6
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Soussi C, Berthoz S, Chirokoff V, Chanraud S. Interindividual Brain and Behavior Differences in Adaptation to Unexpected Uncertainty. BIOLOGY 2023; 12:1323. [PMID: 37887033 PMCID: PMC10604029 DOI: 10.3390/biology12101323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 09/25/2023] [Accepted: 10/03/2023] [Indexed: 10/28/2023]
Abstract
To adapt to a new environment, individuals must alternate between exploiting previously learned "action-consequence" combinations and exploring new actions for which the consequences are unknown: they face an exploration/exploitation trade-off. The neural substrates of these behaviors and the factors that may relate to the interindividual variability in their expression remain overlooked, in particular when considering neural connectivity patterns. Here, to trigger environmental uncertainty, false feedbacks were introduced in the second phase of an associative learning task. Indices reflecting exploitation and cost of uncertainty were computed. Changes in the intrinsic connectivity were determined using resting-state functional connectivity (rFC) analyses before and after performing the "cheated" phase of the task in the MRI. We explored their links with behavioral and psychological factors. Dispersion in the participants' cost of uncertainty was used to categorize two groups. These groups showed different patterns of rFC changes. Moreover, in the overall sample, exploitation was correlated with rFC changes between (1) the anterior cingulate cortex and the cerebellum region 3, and (2) the left frontal inferior gyrus (orbital part) and the right frontal inferior gyrus (triangular part). Anxiety and doubt about action propensity were weakly correlated with some rFC changes. These results demonstrate that the exploration/exploitation trade-off involves the modulation of cortico-cerebellar intrinsic connectivity.
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Affiliation(s)
- Célia Soussi
- INCIA CNRS 5287, University of Bordeaux, 33076 Bordeaux, France; (C.S.); (V.C.); (S.C.)
- UNICAEN, INSERM, U1237, PhIND “Physiopathology and Imaging of Neurological Disorders”, NeuroPresage Team, Cyceron, Normandy University, 14000 Caen, France
| | - Sylvie Berthoz
- INCIA CNRS 5287, University of Bordeaux, 33076 Bordeaux, France; (C.S.); (V.C.); (S.C.)
- Department of Psychiatry for Adolescents and Young Adults, Institut Mutualiste Montsouris, 75014 Paris, France
| | - Valentine Chirokoff
- INCIA CNRS 5287, University of Bordeaux, 33076 Bordeaux, France; (C.S.); (V.C.); (S.C.)
- Ecole Pratique des Hautes Etudes, Section of Life and Earth Sciences, PSL Research University, 75014 Paris, France
| | - Sandra Chanraud
- INCIA CNRS 5287, University of Bordeaux, 33076 Bordeaux, France; (C.S.); (V.C.); (S.C.)
- Ecole Pratique des Hautes Etudes, Section of Life and Earth Sciences, PSL Research University, 75014 Paris, France
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7
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Tabor A, Constant A. Lifeworlds in pain: a principled method for investigation and intervention. Neurosci Conscious 2023; 2023:niad021. [PMID: 37711314 PMCID: PMC10499064 DOI: 10.1093/nc/niad021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 08/03/2023] [Accepted: 08/23/2023] [Indexed: 09/16/2023] Open
Abstract
The experience of pain spans biological, psychological and sociocultural realms, both basic and complex, it is by turns necessary and devastating. Despite an extensive knowledge of the constituents of pain, the ability to translate this into effective intervention remains limited. It is suggested that current, multiscale, medical approaches, largely informed by the biopsychosocial (BPS) model, attempt to integrate knowledge but are undermined by an epistemological obligation, one that necessitates a prior isolation of the constituent parts. To overcome this impasse, we propose that an anthropological stance needs to be taken, underpinned by a Bayesian apparatus situated in computational psychiatry. Here, pain is presented within the context of lifeworlds, where attention is shifted away from the constituents of experience (e.g. nociception, reward processing and fear-avoidance), towards the dynamic affiliation that occurs between these processes over time. We argue that one can derive a principled method of investigation and intervention for pain from modelling approaches in computational psychiatry. We suggest that these modelling methods provide the necessary apparatus to navigate multiscale ontology and epistemology of pain. Finally, a unified approach to the experience of pain is presented, where the relational, inter-subjective phenomenology of pain is brought into contact with a principled method of translation; in so doing, revealing the conditions and possibilities of lifeworlds in pain.
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Affiliation(s)
- Abby Tabor
- Faculty of Health and Applied Sciences, University of the West of England, Frenchay Campus, Coldharbour Ln, Stoke Gifford, Bristol BS16 1QY, UK
- Centre for Pain Research, University of Bath, Claverton Down, Bath BA2 7AY, UK
| | - Axel Constant
- Department of Engineering and Informatics, The University of Sussex, Chichester 1 Room 002, Falmer, Brighton BN1 9QJ, UK
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8
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Hiser J, Heilicher M, Botsford C, Crombie KM, Bellani J, Azar A, Fonzo G, Nacewicz BM, Cisler JM. Decision-making for concurrent reward and threat is differentially modulated by trauma exposure and PTSD symptom severity. Behav Res Ther 2023; 167:104361. [PMID: 37393833 PMCID: PMC10370461 DOI: 10.1016/j.brat.2023.104361] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 05/01/2023] [Accepted: 06/26/2023] [Indexed: 07/04/2023]
Abstract
Trauma exposure, particularly interpersonal violence (IPV) traumas, are significant risk factors for development of mental health disorders, particularly posttraumatic stress disorder (PTSD). Studies attempting to disentangle mechanisms by which trauma confers risk and maintenance of PTSD have often investigated threat or reward learning in isolation. However, real-world decision-making often involves navigating concurrent and conflicting probabilities for threat and reward. We sought to understand how threat and reward learning interact to impact decision-making, and how these processes are modulated by trauma exposure and PTSD symptom severity. 429 adult participants with a range of trauma exposure and symptom severities completed an online version of the two stage Markov task, where participants make a series of decisions towards the goal of obtaining a reward, that embedded an intermediate threat or neutral image along the sequence of decisions to be made. This task design afforded the possibility to differentiate between threat avoidance vs diminished reward learning in the presence of threat, and whether these two processes reflect model-based vs model-free decision-making. Results demonstrated that trauma exposure severity, particularly IPV exposure, was associated with impairment in model-based learning for reward independent of threat, as well as with model-based threat avoidance. PTSD symptom severity was associated with diminished model-based learning for reward in the presence of threat, consistent with a threat-induced impairment in cognitively-demanding strategies for reward learning, but no evidence of heightened threat avoidance. These results highlight the complex interactions between threat and reward learning as a function of trauma exposure and PTSD symptom severity. Findings have potential implications for treatment augmentation and suggest a need for continued research.
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Affiliation(s)
- Jaryd Hiser
- Department of Psychiatry, University of Wisconsin-Madison, USA
| | | | - Chloe Botsford
- Department of Psychiatry, University of Wisconsin-Madison, USA
| | - Kevin M Crombie
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin, USA
| | - Jaideep Bellani
- Department of Psychiatry, University of Wisconsin-Madison, USA
| | - Ameera Azar
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin, USA; Institute for Early Life Adversity Research, University of Texas at Austin, USA
| | - Greg Fonzo
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin, USA
| | | | - Josh M Cisler
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin, USA; Institute for Early Life Adversity Research, University of Texas at Austin, USA.
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9
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Chu S, Hutcherson C, Ito R, Lee ACH. Elucidating medial temporal and frontal lobe contributions to approach-avoidance conflict decision-making using functional MRI and the hierarchical drift diffusion model. Cereb Cortex 2023; 33:7797-7815. [PMID: 36944537 PMCID: PMC10267625 DOI: 10.1093/cercor/bhad080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 02/09/2023] [Accepted: 02/10/2023] [Indexed: 03/23/2023] Open
Abstract
The prefrontal cortex (PFC) has long been associated with arbitrating between approach and avoidance in the face of conflicting and uncertain motivational information, but recent work has also highlighted medial temporal lobe (MTL) involvement. It remains unclear, however, how the contributions of these regions differ in their resolution of conflict information and uncertainty. We designed an fMRI paradigm in which participants approached or avoided object pairs that differed by motivational conflict and outcome uncertainty (complete certainty vs. complete uncertainty). Behavioral data and decision-making parameters estimated using the hierarchical drift diffusion model revealed that participants' responding was driven by conflict rather than uncertainty. Our neural data suggest that PFC areas contribute to cognitive control during approach-avoidance conflict by potentially adjusting response caution and the strength of evidence generated towards either choice, with differential involvement of anterior cingulate cortex and dorsolateral prefrontal cortex. The MTL, on the other hand, appears to contribute to evidence generation, with the hippocampus linked to evidence accumulation for stimuli. Although findings within perirhinal cortex were comparatively equivocal, some evidence suggests contributions to perceptual representations, particularly under conditions of threat. Our findings provide evidence that MTL and PFC regions may contribute uniquely to arbitrating approach-avoidance conflict.
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Affiliation(s)
- Sonja Chu
- Department of Psychological Clinical Science, University of Toronto, 1265 Military Trail, Toronto, ON M1C 1A4, Canada
| | - Cendri Hutcherson
- Department of Psychological Clinical Science, University of Toronto, 1265 Military Trail, Toronto, ON M1C 1A4, Canada
- Department of Psychology (Scarborough), University of Toronto, 1265 Military Trail, Toronto, ON M1C 1A4, Canada
- Rotman School of Management, University of Toronto, 105 St. George Street, Toronto, ON M5S 3E6, Canada
| | - Rutsuko Ito
- Department of Psychological Clinical Science, University of Toronto, 1265 Military Trail, Toronto, ON M1C 1A4, Canada
- Department of Psychology (Scarborough), University of Toronto, 1265 Military Trail, Toronto, ON M1C 1A4, Canada
- Department of Cell and Systems Biology, University of Toronto, 25 Harbord Street, Toronto, ON M5S 3G5, Canada
| | - Andy C H Lee
- Department of Psychological Clinical Science, University of Toronto, 1265 Military Trail, Toronto, ON M1C 1A4, Canada
- Department of Psychology (Scarborough), University of Toronto, 1265 Military Trail, Toronto, ON M1C 1A4, Canada
- Rotman Research Institute, Baycrest Centre, 3560 Bathurst Street, Toronto, ON M6A 2E1, Canada
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10
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Benrimoh D, Fisher V, Mourgues C, Sheldon AD, Smith R, Powers AR. Barriers and solutions to the adoption of translational tools for computational psychiatry. Mol Psychiatry 2023; 28:2189-2196. [PMID: 37280282 PMCID: PMC10611570 DOI: 10.1038/s41380-023-02114-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 04/25/2023] [Accepted: 05/05/2023] [Indexed: 06/08/2023]
Abstract
Computational psychiatry is a field aimed at developing formal models of information processing in the human brain, and how alterations in this processing can lead to clinical phenomena. There has been significant progress in the development of tasks and how to model them, presenting an opportunity to incorporate computational psychiatry methodologies into large- scale research projects or into clinical practice. In this viewpoint, we explore some of the barriers to incorporation of computational psychiatry tasks and models into wider mainstream research directions. These barriers include the time required for participants to complete tasks, test-retest reliability, limited ecological validity, as well as practical concerns, such as lack of computational expertise and the expense and large sample sizes traditionally required to validate tasks and models. We then discuss solutions, such as the redesigning of tasks with a view toward feasibility, and the integration of tasks into more ecologically valid and standardized game platforms that can be more easily disseminated. Finally, we provide an example of how one task, the conditioned hallucinations task, might be translated into such a game. It is our hope that interest in the creation of more accessible and feasible computational tasks will help computational methods make more positive impacts on research as well as, eventually, clinical practice.
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Affiliation(s)
- David Benrimoh
- McGill University School of Medicine, Montreal, QC, Canada
| | - Victoria Fisher
- 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
| | - Andrew D Sheldon
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Ryan Smith
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Albert R Powers
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA.
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11
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Konaka Y, Naoki H. Decoding reward-curiosity conflict in decision-making from irrational behaviors. NATURE COMPUTATIONAL SCIENCE 2023; 3:418-432. [PMID: 38177842 PMCID: PMC10768639 DOI: 10.1038/s43588-023-00439-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 03/29/2023] [Indexed: 01/06/2024]
Abstract
Humans and animals are not always rational. They not only rationally exploit rewards but also explore an environment owing to their curiosity. However, the mechanism of such curiosity-driven irrational behavior is largely unknown. Here, we developed a decision-making model for a two-choice task based on the free energy principle, which is a theory integrating recognition and action selection. The model describes irrational behaviors depending on the curiosity level. We also proposed a machine learning method to decode temporal curiosity from behavioral data. By applying it to rat behavioral data, we found that the rat had negative curiosity, reflecting conservative selection sticking to more certain options and that the level of curiosity was upregulated by the expected future information obtained from an uncertain environment. Our decoding approach can be a fundamental tool for identifying the neural basis for reward-curiosity conflicts. Furthermore, it could be effective in diagnosing mental disorders.
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Affiliation(s)
- Yuki Konaka
- Laboratory of Data-Driven Biology, Graduate School of Integrated Sciences for Life, Hiroshima University, Hiroshima, Japan
| | - Honda Naoki
- Laboratory of Data-Driven Biology, Graduate School of Integrated Sciences for Life, Hiroshima University, Hiroshima, Japan.
- Kansei-Brain Informatics Group, Center for Brain, Mind and Kansei Sciences Research, Hiroshima University, Hiroshima, Japan.
- Theoretical Biology Research Group, Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences, Okazaki, Japan.
- Laboratory of Theoretical Biology, Graduate School of Biostudies, Kyoto University, Kyoto, Japan.
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12
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Kato A, Shimomura K, Ognibene D, Parvaz MA, Berner LA, Morita K, Fiore VG. Computational models of behavioral addictions: State of the art and future directions. Addict Behav 2023; 140:107595. [PMID: 36621045 DOI: 10.1016/j.addbeh.2022.107595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 11/23/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
Non-pharmacological behavioral addictions, such as pathological gambling, videogaming, social networking, or internet use, are becoming major public health concerns. It is not yet clear how behavioral addictions could share many major neurobiological and behavioral characteristics with substance use disorders, despite the absence of direct pharmacological influences. A deeper understanding of the neurocognitive mechanisms of addictive behavior is needed, and computational modeling could be one promising approach to explain intricately entwined cognitive and neural dynamics. This review describes computational models of addiction based on reinforcement learning algorithms, Bayesian inference, and biophysical neural simulations. We discuss whether computational frameworks originally conceived to explain maladaptive behavior in substance use disorders can be effectively extended to non-substance-related behavioral addictions. Moreover, we introduce recent studies on behavioral addictions that exemplify the possibility of such extension and propose future directions.
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Affiliation(s)
- Ayaka Kato
- RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Kanji Shimomura
- Physical and Health Education, Graduate School of Education, The University of Tokyo, Tokyo 113-0033, Japan
| | - Dimitri Ognibene
- Department of Psychology, Università degli Studi Milano-Bicocca, Milan, Italy; School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
| | - Muhammad A Parvaz
- Departments of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Laura A Berner
- Center of Excellence in Eating and Weight Disorders, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Center for Computational Psychiatry, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kenji Morita
- Physical and Health Education, Graduate School of Education, The University of Tokyo, Tokyo 113-0033, Japan; International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo, Tokyo 113-0033, Japan
| | - Vincenzo G Fiore
- Center for Computational Psychiatry, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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13
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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 : THE PREPRINT SERVER FOR HEALTH SCIENCES 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] [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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14
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Letkiewicz AM, Kottler HC, Shankman SA, Cochran AL. Quantifying aberrant approach-avoidance conflict in psychopathology: A review of computational approaches. Neurosci Biobehav Rev 2023; 147:105103. [PMID: 36804398 PMCID: PMC10023482 DOI: 10.1016/j.neubiorev.2023.105103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 02/02/2023] [Accepted: 02/16/2023] [Indexed: 02/19/2023]
Abstract
Making effective decisions during approach-avoidance conflict is critical in daily life. Aberrant decision-making during approach-avoidance conflict is evident in a range of psychological disorders, including anxiety, depression, trauma-related disorders, substance use disorders, and alcohol use disorders. To help clarify etiological pathways and reveal novel intervention targets, clinical research into decision-making is increasingly adopting a computational psychopathology approach. This approach uses mathematical models that can identify specific decision-making related processes that are altered in mental health disorders. In our review, we highlight foundational approach-avoidance conflict research, followed by more in-depth discussion of computational approaches that have been used to model behavior in these tasks. Specifically, we describe the computational models that have been applied to approach-avoidance conflict (e.g., drift-diffusion, active inference, and reinforcement learning models), and provide resources to guide clinical researchers who may be interested in applying computational modeling. Finally, we identify notable gaps in the current literature and potential future directions for computational approaches aimed at identifying mechanisms of approach-avoidance conflict in psychopathology.
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Affiliation(s)
- Allison M Letkiewicz
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, USA.
| | - Haley C Kottler
- Department of Mathematics, University of Wisconsin, Madison, WI, USA
| | - Stewart A Shankman
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, USA; Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Amy L Cochran
- Department of Mathematics, University of Wisconsin, Madison, WI, USA; Department of Population Health Sciences, University of Wisconsin, Madison, WI, USA
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15
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Friston K. Computational psychiatry: from synapses to sentience. Mol Psychiatry 2023; 28:256-268. [PMID: 36056173 PMCID: PMC7614021 DOI: 10.1038/s41380-022-01743-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Revised: 08/08/2022] [Accepted: 08/11/2022] [Indexed: 01/09/2023]
Abstract
This review considers computational psychiatry from a particular viewpoint: namely, a commitment to explaining psychopathology in terms of pathophysiology. It rests on the notion of a generative model as underwriting (i) sentient processing in the brain, and (ii) the scientific process in psychiatry. The story starts with a view of the brain-from cognitive and computational neuroscience-as an organ of inference and prediction. This offers a formal description of neuronal message passing, distributed processing and belief propagation in neuronal networks; and how certain kinds of dysconnection lead to aberrant belief updating and false inference. The dysconnections in question can be read as a pernicious synaptopathy that fits comfortably with formal notions of how we-or our brains-encode uncertainty or its complement, precision. It then considers how the ensuing process theories are tested empirically, with an emphasis on the computational modelling of neuronal circuits and synaptic gain control that mediates attentional set, active inference, learning and planning. The opportunities afforded by this sort of modelling are considered in light of in silico experiments; namely, computational neuropsychology, computational phenotyping and the promises of a computational nosology for psychiatry. The resulting survey of computational approaches is not scholarly or exhaustive. Rather, its aim is to review a theoretical narrative that is emerging across subdisciplines within psychiatry and empirical scales of investigation. These range from epilepsy research to neurodegenerative disorders; from post-traumatic stress disorder to the management of chronic pain, from schizophrenia to functional medical symptoms.
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Affiliation(s)
- Karl Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, WC1N 3AR, UK.
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16
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Yamamori Y, Robinson OJ. Computational perspectives on human fear and anxiety. Neurosci Biobehav Rev 2023; 144:104959. [PMID: 36375584 PMCID: PMC10564627 DOI: 10.1016/j.neubiorev.2022.104959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 10/25/2022] [Accepted: 11/09/2022] [Indexed: 11/12/2022]
Abstract
Fear and anxiety are adaptive emotions that serve important defensive functions, yet in excess, they can be debilitating and lead to poor mental health. Computational modelling of behaviour provides a mechanistic framework for understanding the cognitive and neurobiological bases of fear and anxiety, and has seen increasing interest in the field. In this brief review, we discuss recent developments in the computational modelling of human fear and anxiety. Firstly, we describe various reinforcement learning strategies that humans employ when learning to predict or avoid threat, and how these relate to symptoms of fear and anxiety. Secondly, we discuss initial efforts to explore, through a computational lens, approach-avoidance conflict paradigms that are popular in animal research to measure fear- and anxiety-relevant behaviours. Finally, we discuss negative biases in decision-making in the face of uncertainty in anxiety.
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Affiliation(s)
- Yumeya Yamamori
- Institute of Cognitive Neuroscience, University College London, UK.
| | - Oliver J Robinson
- Institute of Cognitive Neuroscience, University College London, UK; Clinical, Educational and Health Psychology, University College London, UK
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17
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Hoppe JM, Vegelius J, Gingnell M, Björkstrand J, Frick A. Internet-delivered approach-avoidance conflict task shows temporal stability and relation to trait anxiety. LEARNING AND MOTIVATION 2022. [DOI: 10.1016/j.lmot.2022.101848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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18
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Gijsen S, Grundei M, Blankenburg F. Active inference and the two-step task. Sci Rep 2022; 12:17682. [PMID: 36271279 PMCID: PMC9586964 DOI: 10.1038/s41598-022-21766-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 09/30/2022] [Indexed: 01/18/2023] Open
Abstract
Sequential decision problems distill important challenges frequently faced by humans. Through repeated interactions with an uncertain world, unknown statistics need to be learned while balancing exploration and exploitation. Reinforcement learning is a prominent method for modeling such behaviour, with a prevalent application being the two-step task. However, recent studies indicate that the standard reinforcement learning model sometimes describes features of human task behaviour inaccurately and incompletely. We investigated whether active inference, a framework proposing a trade-off to the exploration-exploitation dilemma, could better describe human behaviour. Therefore, we re-analysed four publicly available datasets of the two-step task, performed Bayesian model selection, and compared behavioural model predictions. Two datasets, which revealed more model-based inference and behaviour indicative of directed exploration, were better described by active inference, while the models scored similarly for the remaining datasets. Learning using probability distributions appears to contribute to the improved model fits. Further, approximately half of all participants showed sensitivity to information gain as formulated under active inference, although behavioural exploration effects were not fully captured. These results contribute to the empirical validation of active inference as a model of human behaviour and the study of alternative models for the influential two-step task.
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Affiliation(s)
- Sam Gijsen
- grid.14095.390000 0000 9116 4836Neurocomputation and Neuroimaging Unit, Freie Universität Berlin, 14195 Berlin, Germany ,grid.7468.d0000 0001 2248 7639Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Miro Grundei
- grid.14095.390000 0000 9116 4836Neurocomputation and Neuroimaging Unit, Freie Universität Berlin, 14195 Berlin, Germany ,grid.7468.d0000 0001 2248 7639Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Felix Blankenburg
- grid.14095.390000 0000 9116 4836Neurocomputation and Neuroimaging Unit, Freie Universität Berlin, 14195 Berlin, Germany ,grid.7468.d0000 0001 2248 7639Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
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McGovern HT, De Foe A, Biddell H, Leptourgos P, Corlett P, Bandara K, Hutchinson BT. Learned uncertainty: The free energy principle in anxiety. Front Psychol 2022; 13:943785. [PMID: 36248528 PMCID: PMC9559819 DOI: 10.3389/fpsyg.2022.943785] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 07/21/2022] [Indexed: 11/13/2022] Open
Abstract
Generalized anxiety disorder is among the world’s most prevalent psychiatric disorders and often manifests as persistent and difficult to control apprehension. Despite its prevalence, there is no integrative, formal model of how anxiety and anxiety disorders arise. Here, we offer a perspective derived from the free energy principle; one that shares similarities with established constructs such as learned helplessness. Our account is simple: anxiety can be formalized as learned uncertainty. A biological system, having had persistent uncertainty in its past, will expect uncertainty in its future, irrespective of whether uncertainty truly persists. Despite our account’s intuitive simplicity—which can be illustrated with the mere flip of a coin—it is grounded within the free energy principle and hence situates the formation of anxiety within a broader explanatory framework of biological self-organization and self-evidencing. We conclude that, through conceptualizing anxiety within a framework of working generative models, our perspective might afford novel approaches in the clinical treatment of anxiety and its key symptoms.
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Affiliation(s)
- H. T. McGovern
- School of Psychology, The University of Queensland, Brisbane, QLD, Australia
| | - Alexander De Foe
- School of Educational Psychology and Counselling, Monash University, Melbourne, VIC, Australia
| | - Hannah Biddell
- School of Psychology, The University of Queensland, Brisbane, QLD, Australia
| | - Pantelis Leptourgos
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Philip Corlett
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Kavindu Bandara
- School of Psychological Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Brendan T. Hutchinson
- Research School of Psychology, The Australian National University, Canberra, ACT, Australia
- *Correspondence: Brendan T. Hutchinson,
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20
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McDermott TJ, Berg H, Touthang J, Akeman E, Cannon MJ, Santiago J, Cosgrove KT, Clausen AN, Kirlic N, Smith R, Craske MG, Abelson JL, Paulus MP, Aupperle RL. Striatal reactivity during emotion and reward relates to approach-avoidance conflict behaviour and is altered in adults with anxiety or depression. J Psychiatry Neurosci 2022; 47:E311-E322. [PMID: 36223130 PMCID: PMC9448414 DOI: 10.1503/jpn.220083] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 06/24/2022] [Accepted: 06/28/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND We have previously reported activation in reward, salience and executive control regions during functional MRI (fMRI) using an approach-avoidance conflict (AAC) decision-making task with healthy adults. Further investigations into how anxiety and depressive disorders relate to differences in neural responses during AAC can inform their understanding and treatment. We tested the hypothesis that people with anxiety or depression have altered neural activation during AAC. METHODS We compared 118 treatment-seeking adults with anxiety or depression and 58 healthy adults using linear mixed-effects models to examine group-level differences in neural activation (fMRI) during AAC decision-making. Correlational analyses examined relationships between behavioural and neural measures. RESULTS Adults with anxiety or depression had greater striatal engagement when reacting to affective stimuli (p = 0.008, d = 0.31) regardless of valence, and weaker striatal engagement during reward feedback (p = 0.046, d = -0.27) regardless of the presence of monetary reward. They also had blunted amygdala activity during decision-making (p = 0.023, d = -0.32) regardless of the presence of conflict. Across groups, approach behaviour during conflict decision-making was inversely correlated with striatal activation during affective stimuli (p < 0.001, r = -0.28) and positively related to striatal activation during reward feedback (p < 0.001, r = 0.27). LIMITATIONS Our transdiagnostic approach did not allow for comparisons between specific anxiety disorders, and our cross-sectional approach did not allow for causal inference. CONCLUSION Anxiety and depression were associated with altered neural responses to AAC. Findings were consistent with the role of the striatum in action selection and reward responsivity, and they point toward striatal reactivity as a future treatment target. Blunting of amygdala activity in anxiety or depression may indicate a compensatory response to inhibit affective salience and maintain approach.
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Affiliation(s)
- Timothy J McDermott
- From the Laureate Institute for Brain Research, Tulsa, OK (McDermott, Berg, Touthang, Akeman, Cannon, Santiago, Cosgrove, Clausen, Kirlic, Smith, Paulus, Aupperle); the Department of Psychology, University of Tulsa, Tulsa, OK (McDermott, Cosgrove); the Department of Psychology, University of Minnesota-Twin Cities, Minneapolis, MN (Berg); the Department of Psychology and Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA (Craske); the Department of Psychiatry, University of Michigan, Ann Arbor, MI (Abelson); the Department of Community Medicine, University of Tulsa, Tulsa, OK (Paulus, Aupperle)
| | - Hannah Berg
- From the Laureate Institute for Brain Research, Tulsa, OK (McDermott, Berg, Touthang, Akeman, Cannon, Santiago, Cosgrove, Clausen, Kirlic, Smith, Paulus, Aupperle); the Department of Psychology, University of Tulsa, Tulsa, OK (McDermott, Cosgrove); the Department of Psychology, University of Minnesota-Twin Cities, Minneapolis, MN (Berg); the Department of Psychology and Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA (Craske); the Department of Psychiatry, University of Michigan, Ann Arbor, MI (Abelson); the Department of Community Medicine, University of Tulsa, Tulsa, OK (Paulus, Aupperle)
| | - James Touthang
- From the Laureate Institute for Brain Research, Tulsa, OK (McDermott, Berg, Touthang, Akeman, Cannon, Santiago, Cosgrove, Clausen, Kirlic, Smith, Paulus, Aupperle); the Department of Psychology, University of Tulsa, Tulsa, OK (McDermott, Cosgrove); the Department of Psychology, University of Minnesota-Twin Cities, Minneapolis, MN (Berg); the Department of Psychology and Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA (Craske); the Department of Psychiatry, University of Michigan, Ann Arbor, MI (Abelson); the Department of Community Medicine, University of Tulsa, Tulsa, OK (Paulus, Aupperle)
| | - Elisabeth Akeman
- From the Laureate Institute for Brain Research, Tulsa, OK (McDermott, Berg, Touthang, Akeman, Cannon, Santiago, Cosgrove, Clausen, Kirlic, Smith, Paulus, Aupperle); the Department of Psychology, University of Tulsa, Tulsa, OK (McDermott, Cosgrove); the Department of Psychology, University of Minnesota-Twin Cities, Minneapolis, MN (Berg); the Department of Psychology and Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA (Craske); the Department of Psychiatry, University of Michigan, Ann Arbor, MI (Abelson); the Department of Community Medicine, University of Tulsa, Tulsa, OK (Paulus, Aupperle)
| | - Mallory J Cannon
- From the Laureate Institute for Brain Research, Tulsa, OK (McDermott, Berg, Touthang, Akeman, Cannon, Santiago, Cosgrove, Clausen, Kirlic, Smith, Paulus, Aupperle); the Department of Psychology, University of Tulsa, Tulsa, OK (McDermott, Cosgrove); the Department of Psychology, University of Minnesota-Twin Cities, Minneapolis, MN (Berg); the Department of Psychology and Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA (Craske); the Department of Psychiatry, University of Michigan, Ann Arbor, MI (Abelson); the Department of Community Medicine, University of Tulsa, Tulsa, OK (Paulus, Aupperle)
| | - Jessica Santiago
- From the Laureate Institute for Brain Research, Tulsa, OK (McDermott, Berg, Touthang, Akeman, Cannon, Santiago, Cosgrove, Clausen, Kirlic, Smith, Paulus, Aupperle); the Department of Psychology, University of Tulsa, Tulsa, OK (McDermott, Cosgrove); the Department of Psychology, University of Minnesota-Twin Cities, Minneapolis, MN (Berg); the Department of Psychology and Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA (Craske); the Department of Psychiatry, University of Michigan, Ann Arbor, MI (Abelson); the Department of Community Medicine, University of Tulsa, Tulsa, OK (Paulus, Aupperle)
| | - Kelly T Cosgrove
- From the Laureate Institute for Brain Research, Tulsa, OK (McDermott, Berg, Touthang, Akeman, Cannon, Santiago, Cosgrove, Clausen, Kirlic, Smith, Paulus, Aupperle); the Department of Psychology, University of Tulsa, Tulsa, OK (McDermott, Cosgrove); the Department of Psychology, University of Minnesota-Twin Cities, Minneapolis, MN (Berg); the Department of Psychology and Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA (Craske); the Department of Psychiatry, University of Michigan, Ann Arbor, MI (Abelson); the Department of Community Medicine, University of Tulsa, Tulsa, OK (Paulus, Aupperle)
| | - Ashley N Clausen
- From the Laureate Institute for Brain Research, Tulsa, OK (McDermott, Berg, Touthang, Akeman, Cannon, Santiago, Cosgrove, Clausen, Kirlic, Smith, Paulus, Aupperle); the Department of Psychology, University of Tulsa, Tulsa, OK (McDermott, Cosgrove); the Department of Psychology, University of Minnesota-Twin Cities, Minneapolis, MN (Berg); the Department of Psychology and Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA (Craske); the Department of Psychiatry, University of Michigan, Ann Arbor, MI (Abelson); the Department of Community Medicine, University of Tulsa, Tulsa, OK (Paulus, Aupperle)
| | - Namik Kirlic
- From the Laureate Institute for Brain Research, Tulsa, OK (McDermott, Berg, Touthang, Akeman, Cannon, Santiago, Cosgrove, Clausen, Kirlic, Smith, Paulus, Aupperle); the Department of Psychology, University of Tulsa, Tulsa, OK (McDermott, Cosgrove); the Department of Psychology, University of Minnesota-Twin Cities, Minneapolis, MN (Berg); the Department of Psychology and Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA (Craske); the Department of Psychiatry, University of Michigan, Ann Arbor, MI (Abelson); the Department of Community Medicine, University of Tulsa, Tulsa, OK (Paulus, Aupperle)
| | - Ryan Smith
- From the Laureate Institute for Brain Research, Tulsa, OK (McDermott, Berg, Touthang, Akeman, Cannon, Santiago, Cosgrove, Clausen, Kirlic, Smith, Paulus, Aupperle); the Department of Psychology, University of Tulsa, Tulsa, OK (McDermott, Cosgrove); the Department of Psychology, University of Minnesota-Twin Cities, Minneapolis, MN (Berg); the Department of Psychology and Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA (Craske); the Department of Psychiatry, University of Michigan, Ann Arbor, MI (Abelson); the Department of Community Medicine, University of Tulsa, Tulsa, OK (Paulus, Aupperle)
| | - Michelle G Craske
- From the Laureate Institute for Brain Research, Tulsa, OK (McDermott, Berg, Touthang, Akeman, Cannon, Santiago, Cosgrove, Clausen, Kirlic, Smith, Paulus, Aupperle); the Department of Psychology, University of Tulsa, Tulsa, OK (McDermott, Cosgrove); the Department of Psychology, University of Minnesota-Twin Cities, Minneapolis, MN (Berg); the Department of Psychology and Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA (Craske); the Department of Psychiatry, University of Michigan, Ann Arbor, MI (Abelson); the Department of Community Medicine, University of Tulsa, Tulsa, OK (Paulus, Aupperle)
| | - James L Abelson
- From the Laureate Institute for Brain Research, Tulsa, OK (McDermott, Berg, Touthang, Akeman, Cannon, Santiago, Cosgrove, Clausen, Kirlic, Smith, Paulus, Aupperle); the Department of Psychology, University of Tulsa, Tulsa, OK (McDermott, Cosgrove); the Department of Psychology, University of Minnesota-Twin Cities, Minneapolis, MN (Berg); the Department of Psychology and Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA (Craske); the Department of Psychiatry, University of Michigan, Ann Arbor, MI (Abelson); the Department of Community Medicine, University of Tulsa, Tulsa, OK (Paulus, Aupperle)
| | - Martin P Paulus
- From the Laureate Institute for Brain Research, Tulsa, OK (McDermott, Berg, Touthang, Akeman, Cannon, Santiago, Cosgrove, Clausen, Kirlic, Smith, Paulus, Aupperle); the Department of Psychology, University of Tulsa, Tulsa, OK (McDermott, Cosgrove); the Department of Psychology, University of Minnesota-Twin Cities, Minneapolis, MN (Berg); the Department of Psychology and Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA (Craske); the Department of Psychiatry, University of Michigan, Ann Arbor, MI (Abelson); the Department of Community Medicine, University of Tulsa, Tulsa, OK (Paulus, Aupperle)
| | - Robin L Aupperle
- From the Laureate Institute for Brain Research, Tulsa, OK (McDermott, Berg, Touthang, Akeman, Cannon, Santiago, Cosgrove, Clausen, Kirlic, Smith, Paulus, Aupperle); the Department of Psychology, University of Tulsa, Tulsa, OK (McDermott, Cosgrove); the Department of Psychology, University of Minnesota-Twin Cities, Minneapolis, MN (Berg); the Department of Psychology and Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA (Craske); the Department of Psychiatry, University of Michigan, Ann Arbor, MI (Abelson); the Department of Community Medicine, University of Tulsa, Tulsa, OK (Paulus, Aupperle)
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Krypotos AM, Alves M, Crombez G, Vlaeyen JWS. The role of intolerance of uncertainty when solving the exploration-exploitation dilemma. Int J Psychophysiol 2022; 181:33-39. [PMID: 36007711 DOI: 10.1016/j.ijpsycho.2022.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 08/09/2022] [Accepted: 08/12/2022] [Indexed: 11/16/2022]
Abstract
When making behavioral decisions, individuals need to balance between exploiting known options or exploring new ones. How individuals solve this exploration-exploitation dilemma (EED) is a key research question across psychology, leading to attempting to disentangle the cognitive mechanisms behind it. A potential predictive factor of performance in an EED is intolerance of uncertainty (IU), an individual difference factor referring to the extent to which uncertain situations are reported to be aversive. Here, we present the results of a series of exploratory analyses in which we tested the relationship between IU and performance in an EED task. For this, we compiled data from 3 experiments, in which participants received the opportunity to exploit different movements in order to avoid a painful stimulus and approach rewards. For decomposing performance in this task, we used different computational models previously employed in studies on the EED. Then, the parameters of the winning model were correlated with the scores of participants in the IU scale. Correlational and cluster analyses, within both frequentists and Bayesian frameworks, did not provide strong evidence for a relation between EED and IU, apart from the decay rate and the subscale "tendency to become paralyzed in the face of uncertainty". Given the theoretical relation between EED and IU, we propose research with different experimental paradigms.
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Affiliation(s)
- Angelos-Miltiadis Krypotos
- Department of Clinical Psychology, Utrecht University, the Netherlands; Research Group Health Psychology, KU Leuven, Belgium.
| | - Maryna Alves
- Research Group Health Psychology, KU Leuven, Belgium
| | - Geert Crombez
- Department of Experimental-Clinical and Heath Psychology, Ghent University, Belgium
| | - Johan W S Vlaeyen
- Research Group Health Psychology, KU Leuven, Belgium; Experimental Health Psychology, Maastricht University, the Netherlands
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22
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Smith R, Taylor S, Stewart JL, Guinjoan SM, Ironside M, Kirlic N, Ekhtiari H, White EJ, Zheng H, Kuplicki R, Paulus MP. Slower Learning Rates from Negative Outcomes in Substance Use Disorder over a 1-Year Period and Their Potential Predictive Utility. COMPUTATIONAL PSYCHIATRY (CAMBRIDGE, MASS.) 2022; 6:117-141. [PMID: 38774781 PMCID: PMC11104312 DOI: 10.5334/cpsy.85] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 04/27/2022] [Indexed: 11/20/2022]
Abstract
Computational modelling is a promising approach to parse dysfunctional cognitive processes in substance use disorders (SUDs), but it is unclear how much these processes change during the recovery period. We assessed 1-year follow-up data on a sample of treatment-seeking individuals with one or more SUDs (alcohol, cannabis, sedatives, stimulants, hallucinogens, and/or opioids; N = 83) that were previously assessed at baseline within a prior computational modelling study. Relative to healthy controls (HCs; N = 48), these participants were found at baseline to show altered learning rates and less precise action selection while completing an explore-exploit decision-making task. Here we replicated these analyses when these individuals returned and re-performed the task 1 year later to assess the stability of baseline differences. We also examined whether baseline modelling measures could predict symptoms at follow-up. Bayesian and frequentist analyses indicated that: (a) group differences in learning rates were stable over time (posterior probability = 1); and (b) intra-class correlations (ICCs) between model parameters at baseline and follow-up were significant and ranged from small to moderate (.25 ≤ ICCs ≤ .54). Exploratory analyses also suggested that learning rates and/or information-seeking values at baseline were associated with substance use severity at 1-year follow-up in stimulant and opioid users (.36 ≤ rs ≤ .43). These findings suggest that learning dysfunctions are moderately stable during recovery and could correspond to trait-like vulnerability factors. In addition, computational measures at baseline had some predictive value for changes in substance use severity over time and could be clinically informative.
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Affiliation(s)
- Ryan Smith
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Samuel Taylor
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Jennifer L. Stewart
- Laureate Institute for Brain Research, Tulsa, OK, USA
- Department of Community Medicine, University of Tulsa, Tulsa, OK USA
| | | | | | - Namik Kirlic
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | | | - Evan J. White
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Haixia Zheng
- Laureate Institute for Brain Research, Tulsa, OK, USA
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Smith R, Friston KJ, Whyte CJ. A step-by-step tutorial on active inference and its application to empirical data. JOURNAL OF MATHEMATICAL PSYCHOLOGY 2022; 107:102632. [PMID: 35340847 PMCID: PMC8956124 DOI: 10.1016/j.jmp.2021.102632] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
The active inference framework, and in particular its recent formulation as a partially observable Markov decision process (POMDP), has gained increasing popularity in recent years as a useful approach for modeling neurocognitive processes. This framework is highly general and flexible in its ability to be customized to model any cognitive process, as well as simulate predicted neuronal responses based on its accompanying neural process theory. It also affords both simulation experiments for proof of principle and behavioral modeling for empirical studies. However, there are limited resources that explain how to build and run these models in practice, which limits their widespread use. Most introductions assume a technical background in programming, mathematics, and machine learning. In this paper we offer a step-by-step tutorial on how to build POMDPs, run simulations using standard MATLAB routines, and fit these models to empirical data. We assume a minimal background in programming and mathematics, thoroughly explain all equations, and provide exemplar scripts that can be customized for both theoretical and empirical studies. Our goal is to provide the reader with the requisite background knowledge and practical tools to apply active inference to their own research. We also provide optional technical sections and multiple appendices, which offer the interested reader additional technical details. This tutorial should provide the reader with all the tools necessary to use these models and to follow emerging advances in active inference research.
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Affiliation(s)
- Ryan Smith
- Laureate Institute for Brain Research, Tulsa, OK, USA
| | - Karl J. Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, WC1N 3AR, UK
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24
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Ramstead MJD, Seth AK, Hesp C, Sandved-Smith L, Mago J, Lifshitz M, Pagnoni G, Smith R, Dumas G, Lutz A, Friston K, Constant A. From Generative Models to Generative Passages: A Computational Approach to (Neuro) Phenomenology. REVIEW OF PHILOSOPHY AND PSYCHOLOGY 2022; 13:829-857. [PMID: 35317021 PMCID: PMC8932094 DOI: 10.1007/s13164-021-00604-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 10/28/2021] [Indexed: 12/16/2022]
Abstract
This paper presents a version of neurophenomenology based on generative modelling techniques developed in computational neuroscience and biology. Our approach can be described as computational phenomenology because it applies methods originally developed in computational modelling to provide a formal model of the descriptions of lived experience in the phenomenological tradition of philosophy (e.g., the work of Edmund Husserl, Maurice Merleau-Ponty, etc.). The first section presents a brief review of the overall project to naturalize phenomenology. The second section presents and evaluates philosophical objections to that project and situates our version of computational phenomenology with respect to these projects. The third section reviews the generative modelling framework. The final section presents our approach in detail. We conclude by discussing how our approach differs from previous attempts to use generative modelling to help understand consciousness. In summary, we describe a version of computational phenomenology which uses generative modelling to construct a computational model of the inferential or interpretive processes that best explain this or that kind of lived experience.
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Affiliation(s)
- Maxwell J. D. Ramstead
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- VERSES Research Lab and Spatial Web Foundation, Los Angeles, California USA
| | - Anil K. Seth
- School of Engineering and Informatics, University of Sussex, Brighton, BN1 9QJ UK
- Canadian Institute for Advanced Research (CIFAR), Program on Brain, Mind, and Consciousness, Toronto, Ontario, M5G 1M1 Canada
| | - Casper Hesp
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Department of Psychology, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, Netherlands
- Amsterdam Brain and Cognition Centre, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, Netherlands
- Institute for Advanced Study, University of Amsterdam, Oude Turfmarkt 147, 1012 GC Amsterdam, Netherlands
| | - Lars Sandved-Smith
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Lyon Neuroscience Research Centre, INSERM U1028, CNRS UMR5292, Lyon 1 University, Lyon, France
| | - Jonas Mago
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Integrated Program in Neuroscience, Department of Neuroscience, McGill University, Montreal, Canada
- Division of Social and Transcultural Psychiatry, McGill University, Montreal, Canada
| | - Michael Lifshitz
- Division of Social and Transcultural Psychiatry, McGill University, Montreal, Canada
- Lady Davis Institute for Medical Research, Montreal Jewish General Hospital, Montreal, Canada
| | - Giuseppe Pagnoni
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
- Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Modena, Italy
| | - Ryan Smith
- Laureate Institute for Brain Research, Tulsa, Oklahoma USA
| | - Guillaume Dumas
- CHU Sainte-Justine Research Center, Department of Psychiatry, University of Montreal, Montreal, Canada
- Mila – Quebec Artificial Intelligence Institute, University of Montreal, Montreal, Canada
| | - Antoine Lutz
- Lyon Neuroscience Research Centre, INSERM U1028, CNRS UMR5292, Lyon 1 University, Lyon, France
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- VERSES Research Lab and Spatial Web Foundation, Los Angeles, California USA
| | - Axel Constant
- Charles Perkins Centre, The University of Sydney, Sydney, Australia
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25
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Palaniyappan L, Venkatasubramanian G. The Bayesian brain and cooperative communication in schizophrenia. J Psychiatry Neurosci 2022; 47:E48-E54. [PMID: 35135834 PMCID: PMC8834248 DOI: 10.1503/jpn.210231] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Affiliation(s)
- Lena Palaniyappan
- From the Department of Psychiatry, Schulich School of Medicine & Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robart Research Institute & Lawson Health Research Institute, London, Ont., Canada (Palaniyappan); and the InSTAR Program, Schizophrenia Clinic, Department of Psychiatry, National Institute of Mental Health and Neuro Sciences, Bangalore, India (Venkatasubramanian)
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26
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Tschantz A, Barca L, Maisto D, Buckley CL, Seth AK, Pezzulo G. Simulating homeostatic, allostatic and goal-directed forms of interoceptive control using active inference. Biol Psychol 2022; 169:108266. [DOI: 10.1016/j.biopsycho.2022.108266] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Revised: 01/06/2022] [Accepted: 01/14/2022] [Indexed: 12/28/2022]
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27
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Cross-species anxiety tests in psychiatry: pitfalls and promises. Mol Psychiatry 2022; 27:154-163. [PMID: 34561614 PMCID: PMC8960405 DOI: 10.1038/s41380-021-01299-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 08/16/2021] [Accepted: 09/08/2021] [Indexed: 11/08/2022]
Abstract
Behavioural anxiety tests in non-human animals are used for anxiolytic drug discovery, and to investigate the neurobiology of threat avoidance. Over the past decade, several of them were translated to humans with three clinically relevant goals: to assess potential efficacy of candidate treatments in healthy humans; to develop diagnostic tests or biomarkers; and to elucidate the pathophysiology of anxiety disorders. In this review, we scrutinise these promises and compare seven anxiety tests that are validated across species: five approach-avoidance conflict tests, unpredictable shock anticipation, and the social intrusion test in children. Regarding the first goal, three tests appear suitable for anxiolytic drug screening in humans. However, they have not become part of the drug development pipeline and achieving this may require independent confirmation of predictive validity and cost-effectiveness. Secondly, two tests have shown potential to measure clinically relevant individual differences, but their psychometric properties, predictive value, and clinical applicability need to be clarified. Finally, cross-species research has not yet revealed new evidence that the physiology of healthy human behaviour in anxiety tests relates to the physiology of anxiety symptoms in patients. To summarise, cross-species anxiety tests could be rendered useful for drug screening and for development of diagnostic instruments. Using these tests for aetiology research in healthy humans or animals needs to be queried and may turn out to be unrealistic.
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28
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Computational Mechanisms of Addiction: Recent Evidence and Its Relevance to Addiction Medicine. CURRENT ADDICTION REPORTS 2021. [DOI: 10.1007/s40429-021-00399-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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29
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Smith R, Kirlic N, Stewart JL, Touthang J, Kuplicki R, McDermott TJ, Taylor S, Khalsa SS, Paulus MP, Aupperle RL. Long-term stability of computational parameters during approach-avoidance conflict in a transdiagnostic psychiatric patient sample. Sci Rep 2021; 11:11783. [PMID: 34083701 PMCID: PMC8175390 DOI: 10.1038/s41598-021-91308-x] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 05/17/2021] [Indexed: 11/16/2022] Open
Abstract
Maladaptive behavior during approach-avoidance conflict (AAC) is common to multiple psychiatric disorders. Using computational modeling, we previously reported that individuals with depression, anxiety, and substance use disorders (DEP/ANX; SUDs) exhibited differences in decision uncertainty and sensitivity to negative outcomes versus reward (emotional conflict) relative to healthy controls (HCs). However, it remains unknown whether these computational parameters and group differences are stable over time. We analyzed 1-year follow-up data from a subset of the same participants (N = 325) to assess parameter stability and relationships to other clinical and task measures. We assessed group differences in the entire sample as well as a subset matched for age and IQ across HCs (N = 48), SUDs (N = 29), and DEP/ANX (N = 121). We also assessed 2-3 week reliability in a separate sample of 30 HCs. Emotional conflict and decision uncertainty parameters showed moderate 1-year intra-class correlations (.52 and .46, respectively) and moderate to excellent correlations over the shorter period (.84 and .54, respectively). Similar to previous baseline findings, parameters correlated with multiple response time measures (ps < .001) and self-reported anxiety (r = .30, p < .001) and decision difficulty (r = .44, p < .001). Linear mixed effects analyses revealed that patients remained higher in decision uncertainty (SUDs, p = .009) and lower in emotional conflict (SUDs, p = .004, DEP/ANX, p = .02) relative to HCs. This computational modelling approach may therefore offer relatively stable markers of transdiagnostic psychopathology.
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Affiliation(s)
- Ryan Smith
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA.
| | - Namik Kirlic
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - Jennifer L Stewart
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - James Touthang
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - Rayus Kuplicki
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - Timothy J McDermott
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - Samuel Taylor
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - Sahib S Khalsa
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - Martin P Paulus
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - Robin L Aupperle
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
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30
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Da Costa L, Parr T, Sengupta B, Friston K. Neural Dynamics under Active Inference: Plausibility and Efficiency of Information Processing. ENTROPY (BASEL, SWITZERLAND) 2021; 23:454. [PMID: 33921298 PMCID: PMC8069154 DOI: 10.3390/e23040454] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 04/06/2021] [Indexed: 02/07/2023]
Abstract
Active inference is a normative framework for explaining behaviour under the free energy principle-a theory of self-organisation originating in neuroscience. It specifies neuronal dynamics for state-estimation in terms of a descent on (variational) free energy-a measure of the fit between an internal (generative) model and sensory observations. The free energy gradient is a prediction error-plausibly encoded in the average membrane potentials of neuronal populations. Conversely, the expected probability of a state can be expressed in terms of neuronal firing rates. We show that this is consistent with current models of neuronal dynamics and establish face validity by synthesising plausible electrophysiological responses. We then show that these neuronal dynamics approximate natural gradient descent, a well-known optimisation algorithm from information geometry that follows the steepest descent of the objective in information space. We compare the information length of belief updating in both schemes, a measure of the distance travelled in information space that has a direct interpretation in terms of metabolic cost. We show that neural dynamics under active inference are metabolically efficient and suggest that neural representations in biological agents may evolve by approximating steepest descent in information space towards the point of optimal inference.
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Affiliation(s)
- Lancelot Da Costa
- Department of Mathematics, Imperial College London, London SW7 2AZ, UK
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK; (T.P.); (B.S.); (K.F.)
| | - Thomas Parr
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK; (T.P.); (B.S.); (K.F.)
| | - Biswa Sengupta
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK; (T.P.); (B.S.); (K.F.)
- Core Machine Learning Group, Zebra AI, London WC2H 8TJ, UK
- Department of Bioengineering, Imperial College London, London SW7 2AZ, UK
| | - Karl Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London WC1N 3BG, UK; (T.P.); (B.S.); (K.F.)
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31
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Smith R, Feinstein JS, Kuplicki R, Forthman KL, Stewart JL, Paulus MP, Khalsa SS. Perceptual insensitivity to the modulation of interoceptive signals in depression, anxiety, and substance use disorders. Sci Rep 2021; 11:2108. [PMID: 33483527 PMCID: PMC7822872 DOI: 10.1038/s41598-021-81307-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 12/28/2020] [Indexed: 01/18/2023] Open
Abstract
This study employed a series of heartbeat perception tasks to assess the hypothesis that cardiac interoceptive processing in individuals with depression/anxiety (N = 221), and substance use disorders (N = 136) is less flexible than that of healthy individuals (N = 53) in the context of physiological perturbation. Cardiac interoception was assessed via heartbeat tapping when: (1) guessing was allowed; (2) guessing was not allowed; and (3) experiencing an interoceptive perturbation (inspiratory breath hold) expected to amplify cardiac sensation. Healthy participants showed performance improvements across the three conditions, whereas those with depression/anxiety and/or substance use disorder showed minimal improvement. Machine learning analyses suggested that individual differences in these improvements were negatively related to anxiety sensitivity, but explained relatively little variance in performance. These results reveal a perceptual insensitivity to the modulation of interoceptive signals that was evident across several common psychiatric disorders, suggesting that interoceptive deficits in the realm of psychopathology manifest most prominently during states of homeostatic perturbation.
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Affiliation(s)
- Ryan Smith
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | - Justin S Feinstein
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
- Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA
| | - Rayus Kuplicki
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
| | | | - Jennifer L Stewart
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
- Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA
| | - Martin P Paulus
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA
- Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA
| | - Sahib S Khalsa
- Laureate Institute for Brain Research, 6655 S Yale Ave, Tulsa, OK, 74136, USA.
- Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, USA.
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