Exploring the behavioural and neural bases of impulsivity in a transdiagnostic approach relevant for addiction.

Abstract

This thesis aims to investigate the behavioural and neural basis of impulsivity, which is known to be a risk factor in the development of psychiatric disorders such as addiction. To tackle this issue, the thesis uses neuroimaging, behavioural, and machine-learning techniques to identify transdiagnostic markers of impulsivity and their relationship with striatal connectivity and decision-making variability. This thesis was divided into two studies. The first one aims to associate a set of transdiagnostic markers relevant to addiction with the connectivity profiles of the striatum, which is known to be implicated in processes of reward, motivation, and decision-making. In particular, this work highlighted the link between the connectivity of the Nucleus Accumbens core (NAc-core) with the Anterior cingulate cortex (ACC) and of the Nucleus Accumbens shell (NAc-shell) with the Orbitofrontal Cortex (OFC). The findings of this study could have substantial implications for the advancement of personalised impulsive symptom treatments in multiple psychiatric diseases as they establish an unprecedented level of anatomical precision about behaviours. Moreover, the second study, mirroring the first in its substantial sample size, discovered a correlation between markers of impulsivity and variability in credit assignment. This underscores the ambitious scope of the research. Specifically, the study did not emphasize distinct performances in the credit assignment task but rather highlighted variations in learning strategies. In particular, the study found that impulsivity was associated with strengthening the association between an outcome and a stimulus. On the contrary, devaluation of a stimulus-action association was associated with anxiety. Both mechanisms are fundamental to associating correct actions with outcomes and learning from mistakes. The study also strived to construct credit-assignment models using reinforcement learning techniques. There is a pressing need to enhance the models’ performance and interpretability. Specifically, these models should be able to forecast each participant’s behaviour individually in the credit assignment task. Subsequently, future studies will develop a model integrating point allocation to explore how strategies change in response to observed feedback. Additionally, in line with literature reports, attentional bias in credit assignment may be influenced by the history of stimulus presentation, making it an intriguing parameter to consider in future research. Altogether, this thesis provides a novel framework for the study of impulsivity and addiction and highlights the importance of integrating both behavioural and neuroimaging data to understand the mechanisms underlying these disorders. Efforts have now to be made to find specific markers of some psychiatric disorders and to develop models that can be used to predict the behaviour of individuals.

Keywords

Functional Connectivity, Dopamine, Computational Psychiatry, Computational neuroscience, Addiction

Document Type

Thesis

Publication Date

2024

Embargo Period

2025-09-25

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This item is under embargo until 25 September 2025

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