TY  - UNPB
TI  - Algorithms of anxiety:
Computational neuroscience approaches to translational measures of anxiety
Y1  - 2025/01/28/
AV  - public
EP  - 170
N1  - 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.
N2  - 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.
ID  - discovery10203011
UR  - https://discovery.ucl.ac.uk/id/eprint/10203011/
PB  - UCL (University College London)
M1  - Doctoral
A1  - Yamamori, Yumeya
ER  -