Comprehensive review:Computational modelling of Schizophrenia

Computational modelling has been used to address: (1) the variety of symptoms observed in schizophrenia using abstract models of behavior (e.g. Bayesian models - top-down descriptive models of psychopathology); (2) the causes of these symptoms using biologically realistic models involving abnormal neuromodulation and/or receptor imbalance (e.g. connectionist and neural networks - bottom-up realistic models of neural processes). These different levels of analysis have been used to answer different questions (i.e. understanding behavioral vs. neurobiological anomalies) about the nature of the disorder. As such, these computational studies have mostly supported diverging hypotheses of schizophrenia's pathophysiology, resulting in a literature that is not always expanding coherently. Some of these hypotheses are however ripe for revision using novel empirical evidence.Here we present a review that first synthesizes the literature of computational modelling for schizophrenia and psychotic symptoms into categories supporting the dopamine, glutamate, GABA, dysconnection and Bayesian inference hypotheses respectively. Secondly, we compare model predictions against the accumulated empirical evidence and finally we identify specific hypotheses that have been left relatively under-investigated

Computational Models Describe Individual Differences in Cognitive Function and Their Relationships to Mental Health Symptoms

Cognitive alterations have long been reported in patients with mental health disorders, though with inconsistent results. These inconsistencies are likely due to highly heterogeneous diagnostic categories used for recruitment, and imprecise cognitive task measures. This thesis addresses the former by measuring symptoms with continuous questionnaire scales, and the latter by using theory- driven computational models that summarise participant behaviour using a small number of mechanistic parameters. This methodology is applied within the realm of attention set shifting and risky decision making to improve understanding of cognition in mental health, using large samples collected online. Following a general introduction (Chapter 1), Chapter 2 describes the computational approach employed in subsequent experimental chapters. In Chapter 3, we develop models of CANTAB IED (Intra-Extra Dimensional Set Shifting Task) to explore how learning and attention processes lead to differences in attention set shifting ability, and to investigate their relationship with symptoms of compulsivity. The second study (Chapter 4) applies the computational approach to risky decisions with CANTAB CGT (Cambridge Gamble Task) and explores the relationship between model parameters and symptoms of depression and anxiety. The final experimental chapter (Chapter 5) examines whether specific symptoms of anxiety are related to changes in risky decision making, focusing on the relationship between catastrophising and probability weighting. Overall, the computational approach offers increased precision when examining behavioural data. In several chapters we identify moderate relationships between model parameters and demographic variables such as age, gender, and level of education, which often exceed associations with traditional model- agnostic measures. However, relationships with mental health symptoms are minimal in the general population datasets tested here. The general discussion (Chapter 6) considers these findings in relation to the wider field of computational psychiatry, discussing both the limitations of the work presented and possible future directions

Asymmetries Between Gains and Losses in Mood and Decision Making

The thesis begins by exploring a large-scale data set from the smartphone application The Great Brain Experiment. I leverage this sample size to show that gambling for prospective losses (but not gains) increases throughout the day. I introduce the question of how exploring asymmetries between attitudes and responses to gains and losses may provide useful insights in the field of Computational Psychiatry. The next section of the thesis concerns mood and affective states, and their connections to decision-making. I introduce a novel paradigm: the Future Prospects Task, which allows for a comparison between how people feel about choosing between prospective gains and prospective losses, and how they feel about such prospects in the future. Computational modelling reveals that affective responses to losses are greater than responses to gains, demonstrating an affective negativity bias. It also demonstrates that the valence of future prospects has an impact on affective state, and that risky decision-making increases with proximity to positive futures, and conversely decreases in proximity to negative futures. This novel paradigm was adapted for a new smartphone application The Happiness Project and for fMRI. Some of the early pilot results for the smartphone application are presented, and their feasibility for future longitudinal testing discussed. The fMRI paradigm and hypotheses are described in the discussion chapter, as data collection was disrupted due to COVID-19. I also endeavour in the thesis to further extend our understanding of models of affective dynamics, which have become popular in the last decade. I include analyses of robustness, and highlight the statistical issues that should be taken into account with their usag

Allostatic self-efficacy: a metacognitive theory of dyshomeostasis-induced fatigue and depression

This paper outlines a hierarchical Bayesian framework for interoception, homeostatic/allostatic control, and meta-cognition that connects fatigue and depression to the experience of chronic dyshomeostasis. Specifically, viewing interoception as the inversion of a generative model of viscerosensory inputs allows for a formal definition of dyshomeostasis (as chronically enhanced surprise about bodily signals, or, equivalently, low evidence for the brain's model of bodily states) and allostasis (as a change in prior beliefs or predictions which define setpoints for homeostatic reflex arcs). Critically, we propose that the performance of interoceptive-allostatic circuitry is monitored by a metacognitive layer that updates beliefs about the brain's capacity to successfully regulate bodily states (allostatic self-efficacy). In this framework, fatigue and depression can be understood as sequential responses to the interoceptive experience of dyshomeostasis and the ensuing metacognitive diagnosis of low allostatic self-efficacy. While fatigue might represent an early response with adaptive value (cf. sickness behavior), the experience of chronic dyshomeostasis may trigger a generalized belief of low self-efficacy and lack of control (cf. learned helplessness), resulting in depression. This perspective implies alternative pathophysiological mechanisms that are reflected by differential abnormalities in the effective connectivity of circuits for interoception and allostasis. We discuss suitably extended models of effective connectivity that could distinguish these connectivity patterns in individual patients and may help inform differential diagnosis of fatigue and depression in the future

Digital History and Hermeneutics

For doing history in the digital age, we need to investigate the “digital kitchen” as the place where the “raw” is transformed into the “cooked”. The novel field of digital hermeneutics provides a critical and reflexive frame for digital humanities research by acquiring digital literacy and skills. The Doctoral Training Unit "Digital History and Hermeneutics" is applying this new digital practice by reflecting on digital tools and methods

Neurocognitive Mechanisms of Learning and Decision-Making in Adolescent-OCD: A Computational Approach

Early-onset obsessive-compulsive disorder (OCD) is substantially less researched than adult-OCD, resulting in prevalent equivocation surrounding the neurocognitive profile of child-OCD. Research into this area is pivotal as population studies report that youths with OCD struggle significantly in academic settings. In the General Introduction of this thesis, I reviewed existing literature and found that strikingly, young patients do not show impairment on features that are considered both hallmarks of adult OCD and tightly linked to disorder symptomatology, such as response inhibition and cognitive flexibility. Among the characteristics that are thought to be present in children and adolescents with OCD are abnormal decision-making under uncertainty and impaired learning, and I decided to focus on these features as they may be driving poor academic attainment in young people with the disorder. In addition, I sought to investigate other cognitive processes that have not been well-researched in adolescent-OCD but are found to be robustly altered in adult OCD such as goal directed/model-based reasoning, meta-cognition, and feedback sensitivity. I aimed to delineate these various processes using a battery of suitably complex cognitive tasks. Moreover, I highlighted that majority of past studies fail to find differences between young patients and controls due to behavioural signatures being too subtle to be uncovered by standard statistical analyses. Hence, I employed computational modelling of cognitive task data to disentangle latent decision-making processes displayed by adolescents with OCD. In Chapter 2, I modelled data from the Wisconsin Card Sorting task, a frequently used paradigm of cognitive flexibility, and confirmed that youths with OCD show equivalent performance on the task to controls. Only patients on serotonergic medication showed increased response latencies and a tendency to make unique errors (choosing a deck associated with no rule present on the test card). Next, in Chapter 3, I sought to understand instrumental and Pavlovian learning, and whether adolescents with OCD show increased punishment sensitivity on a novel aversive Pavlovian-to Instrumental Transfer paradigm. Once again, patient performance was equivalent to that of controls. Hence, the remaining chapters were dedicated to probing behaviour on probabilistic paradigms. In Chapter 4, I formally investigated model-based and model-free learning using a well-validated two step decision-making task, and fit a reinforcement learning drift diffusion model to both choice and reaction time data. Patients showed increased exploration on the task as well as faster and more erratic decisions compared to controls. Nonetheless, model-based learning was equivalent between groups. In the penultimate chapter, I demonstrate on a predictive-inference task that patients with OCD update their choices more frequently compared to controls independent of prediction error magnitude. Finally, in Chapter 6, I administered a probabilistic reversal learning paradigm to a large sample of 50 adolescent patients and 53 matched controls. Standard analyses revealed a significant reversal learning deficit in patients with OCD, wherein they displayed more errors and a lower propensity to repeat choices following positive feedback during the post-reversal phase. Crucially, computational modelling revealed striking group differences where adolescents with OCD displayed elevated reward learning and lower punishment learning, increased exploration, and decreased perseveration compared to controls. In the General Discussion, I emphasise that atypical learning and decision-making in adolescent-OCD are more pronounced on probabilistic tasks, where task environments are more volatile. Results are partly discussed in the context of the uncertainty model of OCD, where subjective feelings of doubt experienced by patients drive compulsive behaviours such as checking and certainty-seeking in daily life, alongside excessive exploration on probabilistic tasks. I also consider various explanations for cognitive distinctions between adult- and adolescent OCD. More general implications of the findings are discussed for understanding OCD in the context of adolescent development and for treatment/support strategies.WELLCOME TRUST (104631/Z/14/Z

Serotonin modulates asymmetric learning from reward and punishment in healthy human volunteers

Instrumental learning is driven by a history of outcome success and failure. Here, we examined the impact of serotonin on learning from positive and negative outcomes. Healthy human volunteers were assessed twice, once after acute (single-dose), and once after prolonged (week-long) daily administration of the SSRI citalopram or placebo. Using computational modelling, we show that prolonged boosting of serotonin enhances learning from punishment and reduces learning from reward. This valence-dependent learning asymmetry increases subjects’ tendency to avoid actions as a function of cumulative failure without leading to detrimental, or advantageous, outcomes. By contrast, no significant modulation of learning was observed following acute SSRI administration. However, differences between the effects of acute and prolonged administration were not significant. Overall, these findings may help explain how serotonergic agents impact on mood disorders

Getting More out of Biomedical Documents with GATE's Full Lifecycle Open Source Text Analytics.

This software article describes the GATE family of open source text analysis tools and processes. GATE is one of the most widely used systems of its type with yearly download rates of tens of thousands and many active users in both academic and industrial contexts. In this paper we report three examples of GATE-based systems operating in the life sciences and in medicine. First, in genome-wide association studies which have contributed to discovery of a head and neck cancer mutation association. Second, medical records analysis which has significantly increased the statistical power of treatment/ outcome models in the UK’s largest psychiatric patient cohort. Third, richer constructs in drug-related searching. We also explore the ways in which the GATE family supports the various stages of the lifecycle present in our examples. We conclude that the deployment of text mining for document abstraction or rich search and navigation is best thought of as a process, and that with the right computational tools and data collection strategies this process can be made defined and repeatable. The GATE research programme is now 20 years old and has grown from its roots as a specialist development tool for text processing to become a rather comprehensive ecosystem, bringing together software developers, language engineers and research staff from diverse fields. GATE now has a strong claim to cover a uniquely wide range of the lifecycle of text analysis systems. It forms a focal point for the integration and reuse of advances that have been made by many people (the majority outside of the authors’ own group) who work in text processing for biomedicine and other areas. GATE is available online ,1. under GNU open source licences and runs on all major operating systems. Support is available from an active user and developer community and also on a commercial basis