The association between resting-state connectivity and impulsivity in substance addiction
Lead Research Organisation:
Imperial College London
Department Name: Brain Sciences
Abstract
Evidence suggests that increased impulsivity and abnormalities in reward circuitry contribute to the pathophysiology of addiction. However, while dysregulation of the reward system in addiction is well characterised, the neural mechanisms underpinning impulsivity are not as well defined. Whilst trait impulsivity is consistently increased in addicted individuals such results have not been replicated for behavioural measures of impulsivity such as choice and action.
Resting state fMRI (RSfMRI) offers an insight into the functional connectivity (FC) of brain regions. Studies have shown that intrinsic fluctuations in such FC may contribute to addiction neuropathology. For example, associations between FC at rest and clinical variations in personality traits such as impulsivity have previously been shown. This suggests probing FC at rest may allow us to better characterise and understand the neurological nature of impulsivity and how this becomes dysregulated in addiction.
I aim to investigate the association between FC and trait and behavioural measures of impulsivity in order to better define the neural networks associated with this clinical variation in addiction. I hypothesise that dysregulation of FC in prefrontal networks will exist in addiction groups compared with healthy controls and that dysregulation will be differentially associated with various measures of impulsivity. The strongest association will be with subjective measures. Finally, more severe dysregulation will be predictive of relapse.
Abstinent alcohol, cocaine and opiate dependent individuals as well as healthy controls will complete a battery of subjective and behavioural impulsivity measures as well as RSfMRI. FC networks will be identified using dynamic causal modelling and independent component analysis in order to identify differences between healthy controls and addiction groups. Regression analysis will be used to determine the association between impulsivity measures and FC dysregulation. Finally, the association between impulsivity and FC will be probed to determine if this can predict risk of relapse.
Resting state fMRI (RSfMRI) offers an insight into the functional connectivity (FC) of brain regions. Studies have shown that intrinsic fluctuations in such FC may contribute to addiction neuropathology. For example, associations between FC at rest and clinical variations in personality traits such as impulsivity have previously been shown. This suggests probing FC at rest may allow us to better characterise and understand the neurological nature of impulsivity and how this becomes dysregulated in addiction.
I aim to investigate the association between FC and trait and behavioural measures of impulsivity in order to better define the neural networks associated with this clinical variation in addiction. I hypothesise that dysregulation of FC in prefrontal networks will exist in addiction groups compared with healthy controls and that dysregulation will be differentially associated with various measures of impulsivity. The strongest association will be with subjective measures. Finally, more severe dysregulation will be predictive of relapse.
Abstinent alcohol, cocaine and opiate dependent individuals as well as healthy controls will complete a battery of subjective and behavioural impulsivity measures as well as RSfMRI. FC networks will be identified using dynamic causal modelling and independent component analysis in order to identify differences between healthy controls and addiction groups. Regression analysis will be used to determine the association between impulsivity measures and FC dysregulation. Finally, the association between impulsivity and FC will be probed to determine if this can predict risk of relapse.