People constantly decide how much time to invest in more versus less rewarding activities. Foraging tasks, during which participants visit contexts with diminishing reward rates, examine this type of decision-making by measuring when individuals choose to switch contexts. It is widely known that humans and other animals perform suboptimally in these tasks. However, the precise nature of this suboptimality, and its links to other behaviourally relevant traits, remain unclear. Here, we developed a foraging task to disambiguate the impacts of initial reward rates and reward rate changes. We investigated how foraging behaviour differs with age and relates to apathy and depression, which are key factors known to influence reward-based decision-making, while controlling for cognitive factors. In addition to overstaying, we found that participants performed more suboptimally as reward decay rate increased, and that many participants expressed a heuristic preference for staying in the single best condition. Moreover, overstaying was strongly associated with higher scaling of stay durations to each condition, and this overstaying/scaling behaviour was positively associated with age but negatively associated with depressive symptoms. No associations were found between foraging behaviour and apathy. Together, our results suggest that people may counterproductively interpret staying in a patch as persistence in reward extraction, which would explain the tight link between overstaying but high sensitivity to reward conditions, and their association with depressive symptoms.

We discuss the evolution of a computational model of delusions, beginning with a background consideration of how computational psychiatry, with its roots firmly based in cognitive neuropsychiatry, seeks to develop descriptive and mechanistic models that reach across different levels of explanation in order to provide more comprehensive understanding of how neurobiological, cognitive, subjective and sociocultural factors may all contribute to complex psychopathology. This quest for bridging explanations - or "consilience" - across the levels is a shared goal of computational psychiatry and cognitive neuropsychiatry and is, we argue, crucial to explaining delusional beliefs. We outline how early computational models appealed to prediction error disturbances as a basis for understanding the early emergence of delusions and show that, despite empirical support, there have been certain explanatory limitations that make a simple prediction error account partially limited. Embedding the account within the increasingly influential hierarchical predictive processing framework subsequently offered a more powerful and comprehensive account, particularly by encouraging the consideration hierarchicallyorganized inference and its evolution over time. However, further limitations remain in its explanatory scope, most notably the fact that delusions can emerge rapidly and suddenly in a way that seems revelatory and convincing. This phenomenon is not easily encompassed by the standard predictive processing account which emphasizes an iterative process of optimizing inference. However, more recent development in the form of "Hybrid Predictive Coding" posits a complementary rapid inference mechanism. We discuss how this hybrid approach may be key to a more comprehensive computational understanding of delusions. (c) 2024 The Author(s). Published by Elsevier Masson SAS. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

This service evaluation reviewed inclusion of Immersive Virtual Reality (iVR) relaxation activities as part of routine occupational therapy sensory sessions on a specialist dementia unit. Twenty-five sessions were completed over 13 wk with 14 participants. Nine participants chose to engage in multiple sessions. Feasibility was assessed through participant engagement and tolerability. Modal first session length was in the range 30 s to 2 min. This increased to over 2 min on second sessions. There was a lack of significant adverse effects measured by direct questioning, neuropsychiatric assessment before vs. after sessions and adverse incident reporting. Acceptability was assessed via structured review of user and staff feedback which noted positive experiences such as relaxation, openness to discussion, reminiscence, wider engagement and interest in future use. Further work is required to explore efficacy and use in other settings.

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.

Background:To interact successfully with their environment, humans need to build a model to make sense of noisy and ambiguous inputs. An inaccurate model, as suggested to be the case for people with psychosis, disturbs optimal action selection. Recent computational models, such as active inference, have emphasized the importance of action selection, treating it as a key part of the inferential process. Based on an active inference framework, we sought to evaluate previous knowledge and belief precision in an action-based task, given that alterations in these parameters have been linked to the development of psychotic symptoms. We further sought to determine whether task performance and modelling parameters would be suitable for classification of patients and controls. Methods:Twenty-three individuals with an at-risk mental state, 26 patients with first-episode psychosis and 31 controls completed a probabilistic task in which action choice (go/no-go) was dissociated from outcome valence (gain or loss). We evaluated group differences in performance and active inference model parameters and performed receiver operating characteristic (ROC) analyses to assess group classification. Results:We found reduced overall performance in patients with psychosis. Active inference modelling revealed that patients showed increased forgetting, reduced confidence in policy selection and less optimal general choice behaviour, with poorer action-state associations. Importantly, ROC analysis showed fair-to-good classification performance for all groups, when combining modelling parameters and performance measures. Limitations:The sample size is moderate. Conclusion:Active inference modelling of this task provides further explanation for dysfunctional mechanisms underlying decision-making in psychosis and may be relevant for future research on the development of biomarkers for early identification of psychosis.

Over the past few decades, neuroimaging has become a ubiquitoustool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics overtime, in contrast to growth charts for anthropometric traits such as height and weight(1). Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data (http://www.brainchart.io/). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories(2) of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones(3), showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.

all u ci n a tio n fr e q u e n cy.Biolo gic al P syc hi a t ry 9 2 (1 0) , p p. 7 5 2-7 5 3. 1 0. 1 0 1 6/j.biop sy c h. 2 0 2 2. 0 8. 0 2 7 P u blis h e r s p a g e : h t t p:// dx.d oi.o r g/ 1 0. 1 0 1 6/j.biop sy c h. 2 0 2 2. 0 8. 0 2

Abstract Prader–Willi syndrome is a genetic neurodevelopmental disorder with an early phenotype characterized by neonatal hypotonia, failure to thrive, and immature genitalia. The onset of hyperphagia in childhood and developmental, physical and neuropsychiatric characteristics indicate atypical brain development and specifically hypothalamic dysfunction. Whether the latter is a consequence of disruption of hypothalamic pathways for genetic reasons or due to a failure of hypothalamic development remains uncertain. Twenty participants with Prader–Willi syndrome, 40 age-matched controls and 42 obese participants underwent structural MRI scanning. The whole hypothalamus and its subnuclei were segmented from structural acquisitions. The Food-Related Problem Questionnaire was used to provide information relating to eating behaviour. All hypothalamic nuclei were significantly smaller in the Prader–Willi group, compared with age and gender matched controls (P < 0.01) with the exception of the right anterior–inferior nucleus (P = 0.07). Lower whole hypothalamus volume was significantly associated with higher body mass index in Prader–Willi syndrome (P < 0.05). Increased preoccupation with food was associated with lower volumes of the bilateral posterior nuclei and left tubular superior nucleus. The whole hypothalamus and all constituent nuclei were also smaller in Prader–Willi syndrome compared with obese participants (P < 0.001). Connectivity profiles of the hypothalamus revealed that fractional anisotropy was associated with impaired satiety in Prader–Willi syndrome (P < 0.05). We establish that hypothalamic structure is significantly altered in Prader–Willi syndrome, demonstrating that hypothalamic dysfunction linked to eating behaviour is likely neurodevelopmental in nature and furthermore, distinctive compared with obesity in the general population.

Neuroanatomical abnormalities have been reported along a continuum from at-risk stages, including high schizotypy, to early and chronic psychosis. However, a comprehensive neuroanatomical mapping of schizotypy remains to be established. The authors conducted the first large-scale meta-analyses of cortical and subcortical morphometric patterns of schizotypy in healthy individuals, and compared these patterns with neuroanatomical abnormalities observed in major psychiatric disorders. The sample comprised 3004 unmedicated healthy individuals (12–68 years, 46.5% male) from 29 cohorts of the worldwide ENIGMA Schizotypy working group. Cortical and subcortical effect size maps with schizotypy scores were generated using standardized methods. Pattern similarities were assessed between the schizotypy-related cortical and subcortical maps and effect size maps from comparisons of schizophrenia (SZ), bipolar disorder (BD) and major depression (MDD) patients with controls. Thicker right medial orbitofrontal/ventromedial prefrontal cortex (mOFC/vmPFC) was associated with higher schizotypy scores ( r = 0.067, p FDR = 0.02). The cortical thickness profile in schizotypy was positively correlated with cortical abnormalities in SZ ( r = 0.285, p spin = 0.024), but not BD ( r = 0.166, p spin = 0.205) or MDD ( r = −0.274, p spin = 0.073). The schizotypy-related subcortical volume pattern was negatively correlated with subcortical abnormalities in SZ (rho = −0.690, p spin = 0.006), BD (rho = −0.672, p spin = 0.009), and MDD (rho = −0.692, p spin = 0.004). Comprehensive mapping of schizotypy-related brain morphometry in the general population revealed a significant relationship between higher schizotypy and thicker mOFC/vmPFC, in the absence of confounding effects due to antipsychotic medication or disease chronicity. The cortical pattern similarity between schizotypy and schizophrenia yields new insights into a dimensional neurobiological continuity across the extended psychosis phenotype.

AbstractNeuroanatomical abnormalities have been reported along a continuum from at-risk stages, including high schizotypy, to early and chronic psychosis. However, a comprehensive neuroanatomical mapping of schizotypy remains to be established. The authors conducted the first large-scale meta-analyses of cortical and subcortical morphometric patterns of schizotypy in healthy individuals, and compared these patterns with neuroanatomical abnormalities observed in major psychiatric disorders. The sample comprised 3,004 unmedicated healthy individuals (12-68 years, 46.5% male) from 29 cohorts of the worldwide ENIGMA Schizotypy working group. Cortical and subcortical effect size maps with schizotypy scores were generated using standardized methods. Pattern similarities were assessed between the schizotypy-related cortical and subcortical maps and effect size maps from comparisons of schizophrenia (SZ), bipolar disorder (BD) and major depression (MDD) patients with controls. Thicker right medial orbitofrontal/ventromedial prefrontal cortex (mOFC/vmPFC) was associated with higher schizotypy scores (r=.07, pFDR=.02). The cortical thickness profile in schizotypy was positively correlated with cortical abnormalities in SZ (r=.33, pspin=.01), but not BD (r=.19, pspin=.16) or MDD (r=-.22, pspin=.10). The schizotypy-related subcortical volume pattern was negatively correlated with subcortical abnormalities in SZ (rho=-.65, pspin=.01), BD (rho=-.63, pspin=.01), and MDD (rho=-.69, pspin=.004). Comprehensive mapping of schizotypy-related brain morphometry in the general population revealed a significant relationship between higher schizotypy and thicker mOFC/vmPFC, in the absence of confounding effects due to antipsychotic medication or disease chronicity. The cortical pattern similarity between schizotypy and schizophrenia yields new insights into a dimensional neurobiological continuity across the extended psychosis phenotype.

EDITORIAL article Front. Psychol., 18 August 2021 | https://doi.org/10.3389/fpsyg.2021.713107

Health warning labels (HWLs) show promise in reducing motivation towards energy-dense snack foods. Understanding the underlying mechanisms could optimise their effectiveness. In two studies we compared effects of HWLs and irrelevant aversive labels (IALs) on implicit (approach) and explicit (choice) motivation towards unhealthy snacks. We examined whether labelling effects on motivation arose from the creation of outcome-dependent associations between the food and its health consequences (model-based effects) or from simple, non-specific aversive associations (model-free effects). Both label types reduced motivation towards snack foods but only when the label was physically present. HWLs and IALs showed similar effects on implicit motivation, although HWLs reduced explicit motivation more than IALs. Thus, aversive HWLs affect both model-free and model-based processes, the former through low level associative mechanisms affecting implicit motivation, the latter by emphasizing explicit causal links to health outcomes thereby affecting explicitly motivated choice behaviours.

Paul Fletcher is Wellcome Investigator and Bernard Wolfe Professor of Health Neuroscience at the University of Cambridge. He is also Director of Studies for Preclinical Medicine at Clare College and Honorary Consultant Psychiatrist with the Cambridgeshire and Peterborough NHS Foundation Trust. He studied Medicine, before carrying out specialist training in Psychiatry and taking a PhD in cognitive neuroscience. He researches human perception, learning and decision-making in health and mental illness.We do not have direct contact with external reality. We must rely on messages from the sense organs, conveying information about the state of the world and our bodies. These messages are not easy to decipher, being noisy and ambiguous, but from them we have to construct models of the world. I will discuss this challenge and how we are very adept at creating a model of reality based on achieving a balance between what our senses are telling us and our expectations of what should be the case. This is often referred to as the predictive processing framework.Relying on this balance comes at a cost, rendering us vulnerable to illusions and biases and, in more extreme cases, to creating a reality that diverges from that experienced by others. This can arise for a variety of reasons but, at the root, I suggest, lies the nature of the brain as a model-building organ. Though this divergence from reality – psychosis – often seems inexplicable and incomprehensible, I suggest that a few core principles can help us to understand it and offers ways of thinking about how phenomena like hallucinations can be understood. Interestingly, the framework suggests ways in which apparently similar phenomena like hallucinations can arise from distinct alterations to the function of a predictive processing system.

Background Immersive virtual reality (iVR) allows seamless interaction with simulated environments and is becoming an established tool in clinical research. It is unclear whether iVR is acceptable to people with Alzheimer's disease (AD) dementia or useful in their care. We explore whether iVR is a viable research tool that may aid the detection and treatment of AD. Objectives This review examines the use of iVR in people with AD or mild cognitive impairment (MCI). Methods Medline, PsycINFO, Embase, CINAHL, and Web of Science databases were searched from inception. PRISMA guidelines were used with studies selected by at least two researchers. Results Nine studies were eligible for inclusion. None reported any issues with iVR tolerability in participants with MCI and AD on assessment or treatment tasks. One study demonstrated capability for detecting prodromal AD and correlated with neuroanatomical substrates. Two studies showed iVR to have high accuracy in differentiating participants with AD from controls but were not hypothesis driven or with adequate controls measures. In a small validation study and two longitudinal case studies, iVR cognitive training was positively rated but did not demonstrate reliable benefit. Conclusion iVR is emerging as a viable method of assessing older adults and people with AD. Strongest benefits were seen when closely integrated with theoretical models of neurodegeneration and existing screening methods. Further randomized controlled trials integrated with clinical populations are required. This will consolidate the power of iVR for assessment of MCI and clarify treatment efficacy beyond current applications in physical rehabilitation.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Abstract Background Given the global burden of disease of psychotic disorders and the promise of benefit from early intervention, there is an imperative to understand the developmental trajectories from onset of psychotic experiences to clinical disorder and to improve identification of individuals at greatest risk. The aims of this study therefore were: 1) to describe, for the first time, the change in incidence of psychotic experiences in the general population from childhood through early adulthood; 2) to describe the prevalence and burden of unmet clinical need of at-risk mental states and psychotic disorder among young adults in the general population; 3) to examine the predictive ability of both self-reported and interviewer-rated measures of psychotic experiences during childhood and adolescence in identifying psychotic disorder by early adulthood; and 4) to describe longitudinal profiles of psychotic experiences from childhood through early adulthood and investigate a comprehensive range of childhood determinants of symptom persistence. Methods We used data from the ALSPAC birth cohort study. Psychotic experiences and disorder were assessed using semi-structured interviews at ages 12, 18, and 24 (N=7,900 with any data). Incidence rates were estimated using flexible parametric modeling, and positive predictive values (PPVs), sensitivity, specificity, and area under the curve were estimated for prediction. Longitudinal profiles were constructed based on interviewer ratings and frequency of experiences, with profiles describing no experiences (62.5%), episodic experiences (26.5%), persistent/recurrent low frequency (9.1%), and persistent/recurrent high frequency (1.9%) groups. Multinomial regression was used to examine risk factors for persistence, covering socio-demographic, genetic, behavioural, cognitive, and psychological characteristics during childhood. Results The incidence rate of psychotic experiences increased between ages 12 and 24, peaking during late adolescence. A total of 109 individuals (2.8%) met criteria for a psychotic disorder up to age 24, of whom 70% had sought professional help. Prediction of current psychotic disorder at age 24 (N=47, 1.2%), by both self-report and interviewer-rated measures of psychotic experiences at age 18 (PPVs, 2.9% and 10.0%, respectively), was improved by incorporating information on frequency and distress (PPVs, 13.3% and 20.0%, respectively), although sensitivities were low. The PPV of an at-risk mental state at age 18 predicting incident disorder at ages 18–24 was 21.1% (95%CI 6.1, 45.6), and the sensitivity was 14.3% (95%CI 4.0, 32.7). Longitudinal profile analysis showed that persistence was highest in those with higher levels of emotional instability and borderline personality traits in childhood, whilst persistence was strongly related to concurrent and increasing levels of social isolation, anxiety, self-harm, and substance use over time. Discussion Our study results show a peak in incidence of psychotic experience during late adolescence just prior to the peak incidence rate for schizophrenia, and an unmet need for care in young people with psychotic disorders. Although we show the potential efficiency of self-report measures for prediction, because of the low sensitivity, targeting individuals in non-help-seeking samples based only on more severe symptom cutoff thresholds will likely have little impact on population levels of first-episode psychosis. The primary characteristics indexing whether psychotic experiences are likely to persist over time is the presence of emotion regulation difficulties in childhood, providing evidence of a potentially modifiable target for prevention.

Alterations in the balance between prior expectations and sensory evidence may account for faulty perceptions and inferences leading to psychosis. However, uncertainties remain about the nature of altered prior expectations and the degree to which they vary with the emergence of psychosis. We explored how expectations arising at two different levels – cognitive and perceptual – influenced processing of sensory information and whether relative influences of higher and lower level priors differed across people with prodromal symptoms and those with psychotic illness. In two complementary auditory perception experiments, 91 participants (30 with first episode psychosis, 29 at clinical risk for psychosis, and 32 controls) were required to decipher a phoneme within ambiguous auditory input. Expectations were generated in two ways: an accompanying visual input of lip movements observed during auditory presentation, or through written presentation of a phoneme provided prior to auditory presentation. We determined how these different types of information shaped auditory perceptual experience, how this was altered across the prodromal and established phases of psychosis, and how this relates to cingulate glutamate levels assessed by magnetic resonance spectroscopy. The psychosis group relied more on high level cognitive priors compared to both healthy controls and those at clinical risk for psychosis, and more on low level perceptual priors than the clinical risk group. The risk group were marginally less reliant on low level perceptual priors than controls. The results are consistent with previous theory that influences of prior expectations in psychosis in perception differ according to level of prior and illness phase. General scientific summary What we perceive and believe on any given moment will allow us to form expectations about what we will experience in the next. In psychosis, it is believed that the influence of these so-called perceptual and cognitive ‘prior’ expectations on perception is altered, thereby giving rise to the symptoms seen in psychosis. However, research thus far has found mixed evidence, some suggesting an increase in the influence of priors and some finding a decrease. Here we test the hypothesis that perceptual and cognitive priors are differentially affected in individuals at-risk for psychosis and individuals with a first episode of psychosis, thereby partially explaining the mixed findings in the literature. We indeed found evidence in favour of this hypothesis, finding weaker perceptual priors in individuals at-risk, but stronger cognitive priors in individuals with first episode psychosis.

Recent theories of cortical function construe the brain as performing hierarchical Bayesian inference. According to these theories, the precision of prediction errors plays a key role in learning and decision-making, is controlled by dopamine and contributes to the pathogenesis of psychosis. To test these hypotheses, we studied learning with variable outcome-precision in healthy individuals after dopaminergic modulation with a placebo, a dopamine receptor agonist bromocriptine or a dopamine receptor antagonist sulpiride (dopamine study n = 59) and in patients with early psychosis (psychosis study n = 74: 20 participants with first-episode psychosis, 30 healthy controls and 24 participants with at-risk mental state attenuated psychotic symptoms). Behavioural computational modelling indicated that precision weighting of prediction errors benefits learning in health and is impaired in psychosis. FMRI revealed coding of unsigned prediction errors, which signal surprise, relative to their precision in superior frontal cortex (replicated across studies, combined n = 133), which was perturbed by dopaminergic modulation, impaired in psychosis and associated with task performance and schizotypy (schizotypy correlation in 86 healthy volunteers). In contrast to our previous work, we did not observe significant precision-weighting of signed prediction errors, which signal valence, in the midbrain and ventral striatum in the healthy controls (or patients) in the psychosis study. We conclude that healthy people, but not patients with first-episode psychosis, take into account the precision of the environment when updating beliefs. Precision weighting of cortical prediction error signals is a key mechanism through which dopamine modulates inference and contributes to the pathogenesis of psychosis.