1
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Barnby JM, Haslbeck JMB, Rosen C, Sharma R, Harrow M. Modelling the longitudinal dynamics of paranoia in psychosis: A temporal network analysis over 20 years. Schizophr Res 2024; 270:465-475. [PMID: 38996524 DOI: 10.1016/j.schres.2024.06.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 06/28/2024] [Accepted: 06/28/2024] [Indexed: 07/14/2024]
Abstract
BACKGROUND Paranoia is a key feature of psychosis that can be highly debilitating. Theories of paranoia mostly interface with short-scale or cross-sectional data models, leaving the longitudinal course of paranoia underspecified. METHODS We develop an empirical characterisation of two aspects of paranoia - persecutory and referential delusions - in individuals with psychosis over 20 years. We examine delusional dynamics by applying a Graphical Vector Autoregression Model to data collected from the Chicago Follow-up Study (n = 135 with a range of psychosis-spectrum diagnoses). We adjusted for age, sex, IQ, and antipsychotic use. RESULTS We found that referential and persecutory delusions are central themes, supported by other primary delusions, and are strongly autoregressive - the presence of referential and persecutory delusions is predictive of their future occurrence. In a second analysis we demonstrate that social factors influence the severity of referential, but not persecutory, delusions. IMPLICATIONS We suggest that persecutory delusions represent central, resistant states in the cognitive landscape, whereas referential beliefs are more flexible, offering an important window of opportunity for intervention. Our data models can be collated with prior biological, computational, and social work to contribute toward a more complete theory of paranoia and provide more time-dependent evidence for optimal treatment targets.
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Affiliation(s)
- J M Barnby
- Social Computation and Representation Lab, Department of Psychology, Royal Holloway, University of London, London, UK; Cultural and Social Neuroscience Group, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, University of London, London, UK.
| | - J M B Haslbeck
- Department of Clinical Psychological Science, Maastricht University, the Netherlands; Department of Psychological Methods, University of Amsterdam, the Netherlands
| | - C Rosen
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
| | - R Sharma
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
| | - M Harrow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, USA
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2
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Harding JN, Wolpe N, Brugger SP, Navarro V, Teufel C, Fletcher PC. A new predictive coding model for a more comprehensive account of delusions. Lancet Psychiatry 2024; 11:295-302. [PMID: 38242143 DOI: 10.1016/s2215-0366(23)00411-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 11/01/2023] [Accepted: 11/30/2023] [Indexed: 01/21/2024]
Abstract
Attempts to understand psychosis-the experience of profoundly altered perceptions and beliefs-raise questions about how the brain models the world. Standard predictive coding approaches suggest that it does so by minimising mismatches between incoming sensory evidence and predictions. By adjusting predictions, we converge iteratively on a best guess of the nature of the reality. Recent arguments have shown that a modified version of this framework-hybrid predictive coding-provides a better model of how healthy agents make inferences about external reality. We suggest that this more comprehensive model gives us a richer understanding of psychosis compared with standard predictive coding accounts. In this Personal View, we briefly describe the hybrid predictive coding model and show how it offers a more comprehensive account of the phenomenology of delusions, thereby providing a potentially powerful new framework for computational psychiatric approaches to psychosis. We also make suggestions for future work that could be important in formalising this novel perspective.
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Affiliation(s)
- Jessica Niamh Harding
- School of Clinical Medicine, University of Cambridge, Cambridge, UK; Department of Psychiatry, University of Cambridge, Cambridge, UK.
| | - Noham Wolpe
- Department of Psychiatry, University of Cambridge, Cambridge, UK; Department of Physical Therapy, The Stanley Steyer School of Health Professions, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Stefan Peter Brugger
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK; Centre for Academic Mental Health, Bristol Medical school, University of Bristol, Bristol, UK
| | - Victor Navarro
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Christoph Teufel
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Paul Charles Fletcher
- Department of Psychiatry, University of Cambridge, Cambridge, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK; Wellcome Trust MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
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3
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Benrimoh D, Fisher VL, Seabury R, Sibarium E, Mourgues C, Chen D, Powers A. Evidence for Reduced Sensory Precision and Increased Reliance on Priors in Hallucination-Prone Individuals in a General Population Sample. Schizophr Bull 2024; 50:349-362. [PMID: 37830405 PMCID: PMC10919780 DOI: 10.1093/schbul/sbad136] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
BACKGROUND There is increasing evidence that people with hallucinations overweight perceptual beliefs relative to incoming sensory evidence. Past work demonstrating prior overweighting has used simple, nonlinguistic stimuli. However, auditory hallucinations in psychosis are often complex and linguistic. There may be an interaction between the type of auditory information being processed and its perceived quality in engendering hallucinations. STUDY DESIGN We administered a linguistic version of the conditioned hallucinations (CH) task to an online sample of 88 general population participants. Metrics related to hallucination-proneness, hallucination severity, stimulus thresholds, and stimulus detection rates were collected. Data were used to fit parameters of a Hierarchical Gaussian Filter (HGF) model of perceptual inference to determine how latent perceptual states influenced task behavior. STUDY RESULTS Replicating past results, higher CH rates were observed both in those with recent hallucinatory experiences as well as participants with high hallucination-proneness; CH rates were positively correlated with increased prior weighting; and increased prior weighting was related to hallucination severity. Unlike past results, participants with recent hallucinatory experiences as well as those with higher hallucination-proneness had higher stimulus thresholds, lower sensitivity to stimuli presented at the highest threshold, and had lower response confidence, consistent with lower precision of sensory evidence. CONCLUSIONS We replicate the finding that increased CH rates and recent hallucinations correlate with increased prior weighting using a linguistic version of the CH task. Results support a role for reduced sensory precision in the interplay between prior weighting and hallucination-proneness.
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Affiliation(s)
- David Benrimoh
- Department of Psychiatry, McGill University School of Medicine, Montreal, Canada
| | - Victoria L Fisher
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Rashina Seabury
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Ely Sibarium
- 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
| | - Doris Chen
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Albert Powers
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
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4
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López-Silva P, Harrow M, Jobe TH, Tufano M, Harrow H, Rosen C. 'Are these my thoughts?': A 20-year prospective study of thought insertion, thought withdrawal, thought broadcasting, and their relationship to auditory verbal hallucinations. Schizophr Res 2024; 265:46-57. [PMID: 35945121 DOI: 10.1016/j.schres.2022.07.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 06/27/2022] [Accepted: 07/08/2022] [Indexed: 11/22/2022]
Abstract
The co-occurrence of delusions and other symptoms at the onset of psychosis is a challenge for theories about the aetiology of psychosis. This paper explores the relatedness of delusions about the experience of thinking (thought insertion, thought withdrawal, and thought broadcasting) and auditory verbal hallucinations by describing their trajectories over a 20-year period in individuals diagnosed with schizophrenia, affective and other psychosis, and unipolar depression nonpsychosis. The sample consisted of 407 participants who were recruited at index hospitalization and evaluated over six follow-ups over 20 years. The symptom structure associated with thought insertion included auditory verbal hallucinations, somatic hallucinations, other hallucinations, delusions of thought-dissemination, delusions of control, delusion of self-depreciation, depersonalization and anxiety. The symptom constellation of thought withdrawal included somatic hallucinations, other hallucinations, delusions of thought dissemination, delusions of control, sexual delusions, depersonalization, negative symptoms, depression, and anxiety. The symptom constellation of thought broadcasting included auditory verbal hallucinations, somatic hallucinations, delusions of thought-dissemination, delusion of self-depreciation, fantastic delusions, sexual delusions, and depersonalization. Auditory verbal hallucinations and delusions of self-depreciation were significantly associated with both thought insertion and thought broadcasting. Thought insertion and thought withdrawal were significantly associated with other hallucinations, delusions of control, and anxiety; thought withdrawal and thought broadcasting were significantly related to sexual delusions. We hypothesize that specific symptom constellations over time might be explained as the product of pseudo-coherent realities created to give meaning to the experience of the world and the self of individuals in psychosis based on both prior top-down and ongoing bottom-up elements.
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Affiliation(s)
- Pablo López-Silva
- Faculty of Social Sciences, School of Psychology, Universidad de Valparaíso, Chile
| | - Martin Harrow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
| | - Thomas H Jobe
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
| | - Michele Tufano
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
| | - Helen Harrow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
| | - Cherise Rosen
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States.
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5
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Hauke DJ, Wobmann M, Andreou C, Mackintosh AJ, de Bock R, Karvelis P, Adams RA, Sterzer P, Borgwardt S, Roth V, Diaconescu AO. Altered Perception of Environmental Volatility During Social Learning in Emerging Psychosis. COMPUTATIONAL PSYCHIATRY (CAMBRIDGE, MASS.) 2024; 8:1-22. [PMID: 38774429 PMCID: PMC11104374 DOI: 10.5334/cpsy.95] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 12/15/2023] [Indexed: 05/24/2024]
Abstract
Paranoid delusions or unfounded beliefs that others intend to deliberately cause harm are a frequent and burdensome symptom in early psychosis, but their emergence and consolidation still remains opaque. Recent theories suggest that overly precise prediction errors lead to an unstable model of the world providing a breeding ground for delusions. Here, we employ a Bayesian approach to test for such an unstable model of the world and investigate the computational mechanisms underlying emerging paranoia. We modelled behaviour of 18 first-episode psychosis patients (FEP), 19 individuals at clinical high risk for psychosis (CHR-P), and 19 healthy controls (HC) during an advice-taking task designed to probe learning about others' changing intentions. We formulated competing hypotheses comparing the standard Hierarchical Gaussian Filter (HGF), a Bayesian belief updating scheme, with a mean-reverting HGF to model an altered perception of volatility. There was a significant group-by-volatility interaction on advice-taking suggesting that CHR-P and FEP displayed reduced adaptability to environmental volatility. Model comparison favored the standard HGF in HC, but the mean-reverting HGF in CHR-P and FEP in line with perceiving increased volatility, although model attributions in CHR-P were heterogeneous. We observed correlations between perceiving increased volatility and positive symptoms generally as well as with frequency of paranoid delusions specifically. Our results suggest that FEP are characterised by a different computational mechanism - perceiving the environment as increasingly volatile - in line with Bayesian accounts of psychosis. This approach may prove useful to investigate heterogeneity in CHR-P and identify vulnerability for transition to psychosis.
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Affiliation(s)
- Daniel J. Hauke
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Michelle Wobmann
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Christina Andreou
- Department of Psychiatry and Psychotherapy, Translational Psychiatry, University of Lübeck, Lübeck, Germany
| | | | - Renate de Bock
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Povilas Karvelis
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada
| | - Rick A. Adams
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
- Max Planck Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
| | - Philipp Sterzer
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Stefan Borgwardt
- Department of Psychiatry and Psychotherapy, Translational Psychiatry, University of Lübeck, Lübeck, Germany
| | - Volker Roth
- Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
| | - Andreea O. Diaconescu
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health (CAMH), Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
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6
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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] [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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7
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Abstract
BACKGROUND AND HYPOTHESIS The neurocomputational framework of predictive processing (PP) provides a promising approach to explaining delusions, a key symptom of psychotic disorders. According to PP, the brain makes inferences about the world by weighing prior beliefs against the available sensory data. Mismatches between prior beliefs and sensory data result in prediction errors that may update the brain's model of the world. Psychosis has been associated with reduced weighting of priors relative to the sensory data. However, delusional beliefs are highly resistant to change, suggesting increased rather than decreased weighting of priors. We propose that this "delusion paradox" can be resolved within a hierarchical PP model: Reduced weighting of prior beliefs at low hierarchical levels may be compensated by an increased influence of higher-order beliefs represented at high hierarchical levels, including delusional beliefs. This may sculpt perceptual processing into conformity with delusions and foster their resistance to contradictory evidence. STUDY DESIGN We review several lines of experimental evidence on low- and high-level processes, and their neurocognitive underpinnings in delusion-related phenotypes and link them to predicted processing. STUDY RESULTS The reviewed evidence supports the notion of decreased weighting of low-level priors and increased weighting of high-level priors, in both delusional and delusion-prone individuals. Moreover, we highlight the role of prefrontal cortex as a neural basis for the increased weighting of high-level prior beliefs and discuss possible clinical implications of the proposed hierarchical predictive-processing model. CONCLUSIONS Our review suggests the delusion paradox can be resolved within a hierarchical PP model.
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Affiliation(s)
- Predrag Petrovic
- Center for Psychiatry Research (CPF), Center for Cognitive and Computational Neuropsychiatry (CCNP), Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Philipp Sterzer
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
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8
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Eqlimi E, Bockstael A, Schönwiesner M, Talsma D, Botteldooren D. Time course of EEG complexity reflects attentional engagement during listening to speech in noise. Eur J Neurosci 2023; 58:4043-4069. [PMID: 37814423 DOI: 10.1111/ejn.16159] [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: 03/02/2023] [Revised: 08/31/2023] [Accepted: 09/13/2023] [Indexed: 10/11/2023]
Abstract
Auditory distractions are recognized to considerably challenge the quality of information encoding during speech comprehension. This study explores electroencephalography (EEG) microstate dynamics in ecologically valid, noisy settings, aiming to uncover how these auditory distractions influence the process of information encoding during speech comprehension. We examined three listening scenarios: (1) speech perception with background noise (LA), (2) focused attention on the background noise (BA), and (3) intentional disregard of the background noise (BUA). Our findings showed that microstate complexity and unpredictability increased when attention was directed towards speech compared with tasks without speech (LA > BA & BUA). Notably, the time elapsed between the recurrence of microstates increased significantly in LA compared with both BA and BUA. This suggests that coping with background noise during speech comprehension demands more sustained cognitive effort. Additionally, a two-stage time course for both microstate complexity and alpha-to-theta power ratio was observed. Specifically, in the early epochs, a lower level was observed, which gradually increased and eventually reached a steady level in the later epochs. The findings suggest that the initial stage is primarily driven by sensory processes and information gathering, while the second stage involves higher level cognitive engagement, including mnemonic binding and memory encoding.
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Affiliation(s)
- Ehsan Eqlimi
- WAVES Research Group, Department of Information Technology, Ghent University, Ghent, Belgium
| | - Annelies Bockstael
- WAVES Research Group, Department of Information Technology, Ghent University, Ghent, Belgium
| | | | - Durk Talsma
- Department of Experimental Psychology, Ghent University, Ghent, Belgium
| | - Dick Botteldooren
- WAVES Research Group, Department of Information Technology, Ghent University, Ghent, Belgium
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9
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Mikus N, Eisenegger C, Mathys C, Clark L, Müller U, Robbins TW, Lamm C, Naef M. Blocking D2/D3 dopamine receptors in male participants increases volatility of beliefs when learning to trust others. Nat Commun 2023; 14:4049. [PMID: 37422466 PMCID: PMC10329681 DOI: 10.1038/s41467-023-39823-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 06/29/2023] [Indexed: 07/10/2023] Open
Abstract
The ability to learn about other people is crucial for human social functioning. Dopamine has been proposed to regulate the precision of beliefs, but direct behavioural evidence of this is lacking. In this study, we investigate how a high dose of the D2/D3 dopamine receptor antagonist sulpiride impacts learning about other people's prosocial attitudes in a repeated Trust game. Using a Bayesian model of belief updating, we show that in a sample of 76 male participants sulpiride increases the volatility of beliefs, which leads to higher precision weights on prediction errors. This effect is driven by participants with genetically conferred higher dopamine availability (Taq1a polymorphism) and remains even after controlling for working memory performance. Higher precision weights are reflected in higher reciprocal behaviour in the repeated Trust game but not in single-round Trust games. Our data provide evidence that the D2 receptors are pivotal in regulating prediction error-driven belief updating in a social context.
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Affiliation(s)
- Nace Mikus
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria.
- Interacting Minds Centre, Aarhus University, Aarhus, Denmark.
| | - Christoph Eisenegger
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
- Behavioural and Clinical Neuroscience Institute and Department of Psychology, University of Cambridge, Cambridge, UK
| | - Christoph Mathys
- Interacting Minds Centre, Aarhus University, Aarhus, Denmark
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
- Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, Italy
| | - Luke Clark
- Centre for Gambling Research at UBC, Department of Psychology, University of British, Columbia, Vancouver, BC, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Ulrich Müller
- Behavioural and Clinical Neuroscience Institute and Department of Psychology, University of Cambridge, Cambridge, UK
- Adult Neurodevelopmental Services, Health & Community Services, Government of Jersey, St Helier, Jersey
| | - Trevor W Robbins
- Behavioural and Clinical Neuroscience Institute and Department of Psychology, University of Cambridge, Cambridge, UK
| | - Claus Lamm
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria.
| | - Michael Naef
- Department of Economics, University of Durham, Durham, UK.
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10
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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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11
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McFadyen J, Dolan RJ. Spatiotemporal Precision of Neuroimaging in Psychiatry. Biol Psychiatry 2023; 93:671-680. [PMID: 36376110 DOI: 10.1016/j.biopsych.2022.08.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 07/20/2022] [Accepted: 08/12/2022] [Indexed: 12/23/2022]
Abstract
Aberrant patterns of cognition, perception, and behavior seen in psychiatric disorders are thought to be driven by a complex interplay of neural processes that evolve at a rapid temporal scale. Understanding these dynamic processes in vivo in humans has been hampered by a trade-off between spatial and temporal resolutions inherent to current neuroimaging technology. A recent trend in psychiatric research has been the use of high temporal resolution imaging, particularly magnetoencephalography, often in conjunction with sophisticated machine learning decoding techniques. Developments here promise novel insights into the spatiotemporal dynamics of cognitive phenomena, including domains relevant to psychiatric illnesses such as reward and avoidance learning, memory, and planning. This review considers recent advances afforded by exploiting this increased spatiotemporal precision, with specific reference to applications that seek to drive a mechanistic understanding of psychopathology and the realization of preclinical translation.
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Affiliation(s)
- Jessica McFadyen
- UCL Max Planck Centre for Computational Psychiatry and Ageing Research and Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom; State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
| | - Raymond J Dolan
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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12
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Milano BA, Moutoussis M, Convertino L. The neurobiology of functional neurological disorders characterised by impaired awareness. Front Psychiatry 2023; 14:1122865. [PMID: 37009094 PMCID: PMC10060839 DOI: 10.3389/fpsyt.2023.1122865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 02/24/2023] [Indexed: 03/18/2023] Open
Abstract
We review the neurobiology of Functional Neurological Disorders (FND), i.e., neurological disorders not explained by currently identifiable histopathological processes, in order to focus on those characterised by impaired awareness (functionally impaired awareness disorders, FIAD), and especially, on the paradigmatic case of Resignation Syndrome (RS). We thus provide an improved more integrated theory of FIAD, able to guide both research priorities and the diagnostic formulation of FIAD. We systematically address the diverse spectrum of clinical presentations of FND with impaired awareness, and offer a new framework for understanding FIAD. We find that unraveling the historical development of neurobiological theory of FIAD is of paramount importance for its current understanding. Then, we integrate contemporary clinical material in order to contextualise the neurobiology of FIAD within social, cultural, and psychological perspectives. We thus review neuro-computational insights in FND in general, to arrive at a more coherent account of FIAD. FIAD may be based on maladaptive predictive coding, shaped by stress, attention, uncertainty, and, ultimately, neurally encoded beliefs and their updates. We also critically appraise arguments in support of and against such Bayesian models. Finally, we discuss implications of our theoretical account and provide pointers towards an improved clinical diagnostic formulation of FIAD. We suggest directions for future research towards a more unified theory on which future interventions and management strategies could be based, as effective treatments and clinical trial evidence remain limited.
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Affiliation(s)
- Beatrice Annunziata Milano
- Institute of Life Sciences, Sant'Anna School of Advanced Studies, Pisa, Italy
- Faculty of Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Michael Moutoussis
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
- National Hospital of Neurology and Neurosurgery (UCLH), London, United Kingdom
| | - Laura Convertino
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
- National Hospital of Neurology and Neurosurgery (UCLH), London, United Kingdom
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
- *Correspondence: Laura Convertino,
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13
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Tandon R. Computational psychiatry and the psychopathology of psychosis: Promising leads and blind alleys. Schizophr Res 2023; 254:143-145. [PMID: 36889180 DOI: 10.1016/j.schres.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 02/03/2023] [Accepted: 02/04/2023] [Indexed: 03/10/2023]
Affiliation(s)
- Rajiv Tandon
- Department of Psychiatry, WMU Homer Stryker School of Medicine, Kalamazoo, MI, United States of America.
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14
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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: 27] [Impact Index Per Article: 27.0] [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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15
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Priorelli M, Stoianov IP. Flexible intentions: An Active Inference theory. Front Comput Neurosci 2023; 17:1128694. [PMID: 37021085 PMCID: PMC10067605 DOI: 10.3389/fncom.2023.1128694] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 03/03/2023] [Indexed: 04/07/2023] Open
Abstract
We present a normative computational theory of how the brain may support visually-guided goal-directed actions in dynamically changing environments. It extends the Active Inference theory of cortical processing according to which the brain maintains beliefs over the environmental state, and motor control signals try to fulfill the corresponding sensory predictions. We propose that the neural circuitry in the Posterior Parietal Cortex (PPC) compute flexible intentions-or motor plans from a belief over targets-to dynamically generate goal-directed actions, and we develop a computational formalization of this process. A proof-of-concept agent embodying visual and proprioceptive sensors and an actuated upper limb was tested on target-reaching tasks. The agent behaved correctly under various conditions, including static and dynamic targets, different sensory feedbacks, sensory precisions, intention gains, and movement policies; limit conditions were individuated, too. Active Inference driven by dynamic and flexible intentions can thus support goal-directed behavior in constantly changing environments, and the PPC might putatively host its core intention mechanism. More broadly, the study provides a normative computational basis for research on goal-directed behavior in end-to-end settings and further advances mechanistic theories of active biological systems.
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16
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Schimmelpfennig J, Topczewski J, Zajkowski W, Jankowiak-Siuda K. The role of the salience network in cognitive and affective deficits. Front Hum Neurosci 2023; 17:1133367. [PMID: 37020493 PMCID: PMC10067884 DOI: 10.3389/fnhum.2023.1133367] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 02/22/2023] [Indexed: 04/07/2023] Open
Abstract
Analysis and interpretation of studies on cognitive and affective dysregulation often draw upon the network paradigm, especially the Triple Network Model, which consists of the default mode network (DMN), the frontoparietal network (FPN), and the salience network (SN). DMN activity is primarily dominant during cognitive leisure and self-monitoring processes. The FPN peaks during task involvement and cognitive exertion. Meanwhile, the SN serves as a dynamic "switch" between the DMN and FPN, in line with salience and cognitive demand. In the cognitive and affective domains, dysfunctions involving SN activity are connected to a broad spectrum of deficits and maladaptive behavioral patterns in a variety of clinical disorders, such as depression, insomnia, narcissism, PTSD (in the case of SN hyperactivity), chronic pain, and anxiety, high degrees of neuroticism, schizophrenia, epilepsy, autism, and neurodegenerative illnesses, bipolar disorder (in the case of SN hypoactivity). We discuss behavioral and neurological data from various research domains and present an integrated perspective indicating that these conditions can be associated with a widespread disruption in predictive coding at multiple hierarchical levels. We delineate the fundamental ideas of the brain network paradigm and contrast them with the conventional modular method in the first section of this article. Following this, we outline the interaction model of the key functional brain networks and highlight recent studies coupling SN-related dysfunctions with cognitive and affective impairments.
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Affiliation(s)
- Jakub Schimmelpfennig
- Behavioral Neuroscience Lab, Institute of Psychology, SWPS University, Warsaw, Poland
| | - Jan Topczewski
- Behavioral Neuroscience Lab, Institute of Psychology, SWPS University, Warsaw, Poland
| | | | - Kamila Jankowiak-Siuda
- Behavioral Neuroscience Lab, Institute of Psychology, SWPS University, Warsaw, Poland
- *Correspondence: Kamila Jankowiak-Siuda
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17
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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: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [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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18
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Rosen C, Harrow M, Humpston C, Tong L, Jobe TH, Harrow H. 'An experience of meaning': A 20-year prospective analysis of delusional realities in schizophrenia and affective psychoses. Front Psychiatry 2022; 13:940124. [PMID: 35990079 PMCID: PMC9388349 DOI: 10.3389/fpsyt.2022.940124] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 06/24/2022] [Indexed: 11/13/2022] Open
Abstract
Delusions are transdiagnostic and heterogeneous phenomena with varying degrees of intensity, stability, and dimensional attributes where the boundaries between everyday beliefs and delusional beliefs can be experienced as clearly demarcated, fuzzy, or indistinguishable. This highlights the difficulty in defining delusional realities. All individuals in the current study were evaluated at index and at least one of six subsequential follow-ups over 20 years in the Chicago Longitudinal Study. We assessed 16 distinct delusions categorized as thought or thematic delusions. We also examined the probability of recurrence and the relationships between delusions and hallucinations, depression, anxiety, and negative symptoms. The sample consisted of 262 individuals with schizophrenia vs. affective psychosis. Thought delusions were significantly different between groups at all follow-up evaluations except the 20-year timepoint. Thematic delusions were more common than thought delusions and show a significant decreasing pattern. In general, delusional content varied over time. Referential, persecutory, and thought dissemination delusions show the highest probability of recurrence. Hallucinations were the strongest indicator for thought, thematic, and overall delusions. The formation and maintenance of delusions were conceptualized as a multimodal construct consisting of sensory, perceptual, emotional, social, and somatic embodiment of an "experience of meanings". Given the significant associations between delusions and hallucinations, future work incorporating participatory research is needed to better define and align subjective and objective perspectives. Our research also points to the need for future clinical interventions that specifically evaluate and target the coexistence and entanglement of delusions and hallucinations.
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Affiliation(s)
- Cherise Rosen
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
| | - Martin Harrow
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
| | - Clara Humpston
- Department of Psychology, University of York, York, United Kingdom
- School of Psychology, Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom
| | - Liping Tong
- Advocate Aurora Health, Downers Grove, IL, United States
| | - Thomas H. Jobe
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
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19
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Barnby JM, Mehta MA, Moutoussis M. The computational relationship between reinforcement learning, social inference, and paranoia. PLoS Comput Biol 2022; 18:e1010326. [PMID: 35877675 PMCID: PMC9352206 DOI: 10.1371/journal.pcbi.1010326] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 08/04/2022] [Accepted: 06/23/2022] [Indexed: 11/18/2022] Open
Abstract
Theoretical accounts suggest heightened uncertainty about the state of the world underpin aberrant belief updates, which in turn increase the risk of developing a persecutory delusion. However, this raises the question as to how an agent’s uncertainty may relate to the precise phenomenology of paranoia, as opposed to other qualitatively different forms of belief. We tested whether the same population (n = 693) responded similarly to non-social and social contingency changes in a probabilistic reversal learning task and a modified repeated reversal Dictator game, and the impact of paranoia on both. We fitted computational models that included closely related parameters that quantified the rigidity across contingency reversals and the uncertainty about the environment/partner. Consistent with prior work we show that paranoia was associated with uncertainty around a partner’s behavioural policy and rigidity in harmful intent attributions in the social task. In the non-social task we found that pre-existing paranoia was associated with larger decision temperatures and commitment to suboptimal cards. We show relationships between decision temperature in the non-social task and priors over harmful intent attributions and uncertainty over beliefs about partners in the social task. Our results converge across both classes of model, suggesting paranoia is associated with a general uncertainty over the state of the world (and agents within it) that takes longer to resolve, although we demonstrate that this uncertainty is expressed asymmetrically in social contexts. Our model and data allow the representation of sociocognitive mechanisms that explain persecutory delusions and provide testable, phenomenologically relevant predictions for causal experiments.
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Affiliation(s)
- Joseph M. Barnby
- Department of Psychology, Royal Holloway, University of London, London, United Kingdom
- Cultural and Social Neuroscience Group, Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, University of London, London, United Kingdom
- Neuropharmacology Group, Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, University of London, London, United Kingdom
- * E-mail:
| | - Mitul A. Mehta
- Cultural and Social Neuroscience Group, Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, University of London, London, United Kingdom
- Neuropharmacology Group, Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, University of London, London, United Kingdom
| | - Michael Moutoussis
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
- Max-Planck–UCL Centre for Computational Psychiatry and Ageing, University College London, London, United Kingdom
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20
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Fisher VL, Ortiz LS, Powers AR. A computational lens on menopause-associated psychosis. Front Psychiatry 2022; 13:906796. [PMID: 35990063 PMCID: PMC9381820 DOI: 10.3389/fpsyt.2022.906796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 07/07/2022] [Indexed: 11/21/2022] Open
Abstract
Psychotic episodes are debilitating disease states that can cause extreme distress and impair functioning. There are sex differences that drive the onset of these episodes. One difference is that, in addition to a risk period in adolescence and early adulthood, women approaching the menopause transition experience a second period of risk for new-onset psychosis. One leading hypothesis explaining this menopause-associated psychosis (MAP) is that estrogen decline in menopause removes a protective factor against processes that contribute to psychotic symptoms. However, the neural mechanisms connecting estrogen decline to these symptoms are still not well understood. Using the tools of computational psychiatry, links have been proposed between symptom presentation and potential algorithmic and biological correlates. These models connect changes in signaling with symptom formation by evaluating changes in information processing that are not easily observable (latent states). In this manuscript, we contextualize the observed effects of estrogen (decline) on neural pathways implicated in psychosis. We then propose how estrogen could drive changes in latent states giving rise to cognitive and psychotic symptoms associated with psychosis. Using computational frameworks to inform research in MAP may provide a systematic method for identifying patient-specific pathways driving symptoms and simultaneously refine models describing the pathogenesis of psychosis across all age groups.
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Affiliation(s)
- Victoria L Fisher
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, United States
| | - Liara S Ortiz
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, United States
| | - Albert R Powers
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, United States
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21
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Wiese W, Friston KJ. AI ethics in computational psychiatry: From the neuroscience of consciousness to the ethics of consciousness. Behav Brain Res 2021; 420:113704. [PMID: 34871706 PMCID: PMC9125160 DOI: 10.1016/j.bbr.2021.113704] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/25/2021] [Accepted: 11/29/2021] [Indexed: 12/11/2022]
Abstract
Methods used in artificial intelligence (AI) overlap with methods used in computational psychiatry (CP). Hence, considerations from AI ethics are also relevant to ethical discussions of CP. Ethical issues include, among others, fairness and data ownership and protection. Apart from this, morally relevant issues also include potential transformative effects of applications of AI—for instance, with respect to how we conceive of autonomy and privacy. Similarly, successful applications of CP may have transformative effects on how we categorise and classify mental disorders and mental health. Since many mental disorders go along with disturbed conscious experiences, it is desirable that successful applications of CP improve our understanding of disorders involving disruptions in conscious experience. Here, we discuss prospects and pitfalls of transformative effects that CP may have on our understanding of mental disorders. In particular, we examine the concern that even successful applications of CP may fail to take all aspects of disordered conscious experiences into account. Considerations from AI ethics are also relevant to the ethics of computational psychiatry. Ethical issues include, among others, fairness and data ownership and protection. They also include potential transformative effects. Computational psychiatry may transform conceptions of mental disorders and health. Disordered conscious experiences may pose a particular challenge.
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Affiliation(s)
- Wanja Wiese
- Institute of Philosophy II, Ruhr University Bochum, Universitätsstraße 150, 44780 Bochum, Germany.
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London WC1N 3AR, UK
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