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，与持续的执行和情感处理障碍有关。具体而言，苯丙胺依赖者的决策能力较差，特别是在跟踪新信息和利用这一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，这与社会功能低下和复发的可能性较高有关。鉴于情绪在调节和指导决策方面起着重要作用，令人惊讶的是，人们对这些功能障碍如何在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以及在进行新观察时这些信念的动态更新的方法。这样的计算框架可以帮助更好地描绘情绪对个体对选择选项的期望的动态表示的潜在影响，这种累积的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。拟议的研究将使用这种贝叶斯建模方法，结合事件相关的功能磁共振成像（fMRI），以评估潜在的神经认知过程a）在战略和预测过程中的潜在缺陷，指导奖励为基础的决策，在ADI和B）ADI潜在的过度表达或失败，整合负面情绪到这样的决策。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fMRI。几个决策策略和相关的贝叶斯结果预测将估计和相关的神经活动在决策的时候。这些措施将被用来评估和量化ADI和CS之间的差异1）在计算过程中的奖励为基础的决策（目标1）和2）在计算过程中的负面情绪整合到奖励为基础的决策（目标2）。本研究的结果将有助于完善AD的神经认知模型，通过描绘出的计算过程中出差错的ADI，并将有助于确定更精确的神经认知预测的执行和情感功能障碍的ADI。

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.