%0 Thesis
%9 Doctoral
%A Yamamori, Yumeya
%B Institute of Cognitive Neuroscience
%D 2025
%F discovery:10203011
%I UCL (University College London)
%P 170
%T Algorithms of anxiety:  Computational neuroscience approaches to translational measures of anxiety
%U https://discovery.ucl.ac.uk/id/eprint/10203011/
%X Anxiety disorders are among the most prevalent mental health conditions worldwide, yet progress in  developing effective treatments has stagnated. This thesis explores the potential of computational  neuroscience approaches to enhance our understanding of anxiety-related cognition and behaviour,  with a focus on improving translational measures between human and non-human animal research.  The overarching aim was to develop and validate computational models of anxiety-related behaviour  that can be applied across species, framing them as algorithms that can apply to both humans and  non-human animals, and by doing so potentially bridge the gap between preclinical and clinical  research. This work focused on two key paradigms: conditioned tests involving learned approachavoidance conflict, and unconditioned tests examining spontaneous spatial exploration in aversive  environments.  Using large online samples, I first developed and validated a novel human version of classical rodent  conditioned anxiety tests in Chapter 2. Computational modelling revealed that anxiety was  associated with heightened sensitivity to punishments relative to rewards, which mediated  avoidance behaviour. I then conducted two experimental studies manipulating anxiety levels to test  the causal role of this computational mechanism in Chapter 3. Pharmacologically reducing anxiety  with lorazepam shifted sensitivities towards rewards, while threat-of-shock shifted sensitivities  towards punishment and exacerbated avoidance behaviour.  For unconditioned anxiety tests, I proposed a new computational model of spatial exploration in  aversive environments, validated using existing data from a human version of the elevated plus  maze, in Chapter 4. This model provided novel insights into the cognitive mechanisms underlying  anxiolytic and anxiogenic drug effects on exploration behaviour.  Across these studies, the computational approach consistently revealed mechanistic insights that  were not apparent from traditional model-agnostic behavioural analyses alone. The models  developed here offer a more precise, quantitative framework for translating anxiety-related  processes between species. This work demonstrates the potential of computational methods to  enhance the validity and interpretability of translational anxiety research, potentially accelerating the  development of novel treatments.
%Z Copyright © The Author 2024.   Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/).  Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms.  Access may initially be restricted at the author’s request.