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<https://discovery.ucl.ac.uk/id/eprint/10203011> <http://purl.org/ontology/bibo/abstract> "Anxiety disorders are among the most prevalent mental health conditions worldwide, yet progress in\r\ndeveloping effective treatments has stagnated. This thesis explores the potential of computational\r\nneuroscience approaches to enhance our understanding of anxiety-related cognition and behaviour,\r\nwith a focus on improving translational measures between human and non-human animal research.\r\nThe overarching aim was to develop and validate computational models of anxiety-related behaviour\r\nthat can be applied across species, framing them as algorithms that can apply to both humans and\r\nnon-human animals, and by doing so potentially bridge the gap between preclinical and clinical\r\nresearch. This work focused on two key paradigms: conditioned tests involving learned approachavoidance conflict, and unconditioned tests examining spontaneous spatial exploration in aversive\r\nenvironments.\r\nUsing large online samples, I first developed and validated a novel human version of classical rodent\r\nconditioned anxiety tests in Chapter 2. Computational modelling revealed that anxiety was\r\nassociated with heightened sensitivity to punishments relative to rewards, which mediated\r\navoidance behaviour. I then conducted two experimental studies manipulating anxiety levels to test\r\nthe causal role of this computational mechanism in Chapter 3. Pharmacologically reducing anxiety\r\nwith lorazepam shifted sensitivities towards rewards, while threat-of-shock shifted sensitivities\r\ntowards punishment and exacerbated avoidance behaviour.\r\nFor unconditioned anxiety tests, I proposed a new computational model of spatial exploration in\r\naversive environments, validated using existing data from a human version of the elevated plus\r\nmaze, in Chapter 4. This model provided novel insights into the cognitive mechanisms underlying\r\nanxiolytic and anxiogenic drug effects on exploration behaviour.\r\nAcross these studies, the computational approach consistently revealed mechanistic insights that\r\nwere not apparent from traditional model-agnostic behavioural analyses alone. The models\r\ndeveloped here offer a more precise, quantitative framework for translating anxiety-related\r\nprocesses between species. This work demonstrates the potential of computational methods to\r\nenhance the validity and interpretability of translational anxiety research, potentially accelerating the\r\ndevelopment of novel treatments."^^<http://www.w3.org/2001/XMLSchema#string> .
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