The association between resting-state connectivity and impulsivity in substance addiction

有证据表明，冲动的增加和奖赏回路的异常有助于成瘾的病理生理学。然而，虽然成瘾中奖励系统的失调被很好地描述了，但支撑冲动的神经机制却没有被很好地定义。虽然成瘾个体的冲动性特征持续增加，但这种结果并没有被重复用于冲动性的行为测量，如选择和行动。静息状态功能磁共振成像（RSfMRI）提供了对大脑区域功能连接（FC）的深入了解。研究表明，这种FC的内在波动可能导致成瘾神经病理学。例如，休息时的FC与冲动性等人格特征的临床变化之间的关联已经被证明。这表明，在休息时探测FC可能让我们更好地描述和理解冲动的神经本质，以及上瘾时冲动是如何失调的。我的目的是研究FC与冲动性的特征和行为测量之间的关系，以便更好地定义与成瘾临床变异相关的神经网络。我假设，与健康对照组相比，成瘾组前额叶神经网络中FC的失调将存在，而且失调与冲动性的各种测量方法之间存在差异。最强烈的关联将是主观衡量标准。最后，更严重的失调将预示着复发。戒酒、可卡因和阿片类药物依赖者以及健康对照者将完成一系列主观和行为冲动测量以及RSfMRI。将使用动态因果模型和独立成分分析来确定FC网络，以确定健康对照组和成瘾组之间的差异。回归分析将用于确定冲动性测量和FC失调之间的关联。最后，将探讨冲动性和FC之间的关系，以确定这是否可以预测复发的风险。

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.