Bayesian Modeling of Mood Effects on Decision-Making in Amphetamine Dependence

描述（由申请人提供）：安非他明依赖（AD）是一个重要的公共卫生问题，它与持续的执行和情感处理障碍有关。具体来说，安非他明依赖个体（ADI）表现出糟糕的决策能力，特别是在跟踪新信息和利用这些知识指导未来决策方面。此外，ADI在评估和调节负面情绪方面存在困难，这与不良的社会功能和更高的复发可能性有关。考虑到情绪在调节和指导决策方面发挥着重要作用，令人惊讶的是，人们对这种功能障碍如何在ADI中相互作用以及它们如何导致日常功能障碍知之甚少。贝叶斯学习模型提供了一种正式表示个人对环境的信念的方法，并在进行新的观察时动态地更新这些信念。这样的计算框架可以帮助更好地描述情绪对个体对选择选项的期望的动态表示的潜在影响，以及这种累积知识用于预测未来结果的程度，以及个体用于选择行动的认知策略。本研究将使用贝叶斯建模方法，结合事件相关功能磁共振成像（fMRI），来评估以下两方面的神经认知过程：a)指导ADI中基于奖励的决策的战略和预测过程中的潜在缺陷；b) ADI可能过度表达或未能将负面情绪整合到此类决策中。为此，40名ADI和40名健康对照受试者（CS）将在中性和悲伤情绪下执行赌博任务，同时接受功能磁共振成像（fMRI）。几种决策策略和相关的贝叶斯结果预测将被估计，并与决策时的神经活动相关联。这些措施将用于评估和量化ADI和CS之间的差异：1)基于奖励的决策（目标1）的计算过程；2)将负面情绪整合到基于奖励的决策（目标2）的计算过程。本研究的结果将有助于通过描述ADI中出错的计算过程来完善AD的神经认知模型，并将有助于确定更精确的ADI执行和情感功能障碍的神经认知预测因子。

DESCRIPTION (provided by applicant): Amphetamine dependence (AD) is an important public health problem, which has been linked to persistent executive and affective processing impairments. Specifically, amphetamine dependent individuals (ADI) demonstrate poor decision-making, particularly with respect to keeping track of new information and using this knowledge to guide future decisions. In addition, ADI have difficulties in appraising and regulating negative emotion, which has been linked to poor social functioning and higher likelihood of relapse. Given that emotion plays an important role in modulating and guiding decision-making, surprisingly little is known about how such dysfunctions interact in ADI, and how they may contribute to impairments in everyday functioning. Bayesian learning models provide a way to formally represent an individual's beliefs about the environment and the dynamic updating of those beliefs when new observations are made. Such computational framework can help to better delineate the potential impact of emotion on the dynamic representation of individuals' expectations about choice options, on the degree to which this cumulative knowledge is used to predict future outcomes, and on the cognitive strategies individuals use to select actions. The proposed study will use such Bayesian modeling approach, combined with event-related functional magnetic resonance imaging (fMRI), to assess the neurocognitive processes underlying a) potential deficits in the strategic and predictive processes guiding reward-based decision-making in ADI and b) ADI potential overrepresentation of or failure to integrate negative emotion into such decision-making. To do so, 40 ADI and 40 healthy comparison subjects (CS) will perform a gambling task under neutral and sad mood, and while undergoing fMRI. Several decision strategies and the associated Bayesian outcome predictions will be estimated and correlated with neural activity at the time of decision. These measures will be used to assess and quantify differences between ADI and CS 1) in the computational processes underlying reward-based decision (Aim1) and 2) in the computational processes underlying the integration of negative emotion into reward-based decision-making (Aim 2). The outcomes of this study will help to refine neurocognitive models of AD by delineating the computational processes that go awry in ADI and will help identify more precise neurocognitive predictors of executive and affective dysfunction an ADI.