Yamamori, Yumeya;
(2025)
Algorithms of anxiety:
Computational neuroscience approaches to translational measures of anxiety.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
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.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Algorithms of anxiety: Computational neuroscience approaches to translational measures of anxiety |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | 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. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10203011 |
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