Computational Mechanisms of Effort-Cost Decision-Making in Schizophrenia

项目总结： 许多精神分裂症患者的动力减弱，这损害了他们的职业生涯 功能，降低生活质量，增加公共卫生需求。激励的治疗方法 然而，SZ的损害在很大程度上是无效的，部分原因是对病因学的了解很差。近期工作 提供了强有力的证据表明，不正常的工作-成本决策-计算执行来权衡 行动的“成本与收益”--可能是导致精神分裂症患者动力不足的关键因素。具体来说， 研究表明，精神分裂症患者比对照组更不愿意花费精力获得 实验任务的奖赏，这种缺陷与动机障碍有关。然而，由于 使用不精确的实验范式和分析方法，不适合理清 组件流程对努力成本决策的贡献，目前尚不清楚这种减少是否 由对奖励的敏感度降低或对与行动相关的努力的敏感度提高所驱动的。这 知识具有治疗意义，因为针对奖励和努力敏感性的干预措施是不同的。 我们将结合使用实验任务和相关的计算建模方法， 量化努力和奖励敏感度对努力成本决策的相对贡献 精神分裂症和健康对照。我们还将收集基于移动的评估，以提供全面的 日常生活中经历的动机障碍的表型。我们的目标是(A)确定努力成本是否 精神分裂症患者的决策缺陷反映了努力增加或奖励敏感度降低，(B)识别 努力-成本决策损害的神经基础，以及(C)确定努力是否衡量 相当于日常生活中的努力和回报的衡量标准。 很少有研究人员同时接受过临床现象学和计算机方面的培训 建模技术限制了这些方法在理解心理方面可能产生的翻译影响 生病了。考虑到这一点，培训计划是专门设计的，以提供实践指导1)申请 从努力-成本决策到选择行为的计算模型，2)集成计算模型 功能神经成像，以及3)将计算建模参数与基于移动的 对日常激励体验的评估。综上所述，建议的完成将有助 申请者的长期目标是成为一名独立的研究人员，研究计算机制 各种精神疾病中的动机障碍。此外，获得的数据和技能将使 申请者竞争性地提交一份跨诊断R01建议书，旨在检查工作-成本 精神疾病中的决策障碍，其特征是自暴自弃(例如，严重抑郁 精神障碍、精神分裂症)产生于相似或不同的计算机制。

Project Summary: Many people with schizophrenia experience reductions in motivation, which impair occupational functioning, reduce quality of life, and increase public health demands. Treatments for motivational impairments in SZ are largely ineffective, however, in part due to poor understanding of etiology. Recent work has provided strong evidence that abnormal effort-cost decision-making – calculations performed to weigh the “cost vs. benefits” of actions – may be a key contributor to motivational deficits in schizophrenia. Specifically, research has shown that people with schizophrenia are less willing than controls to expend effort to obtain rewards on experimental tasks, and that this deficit is related to motivational impairment. However, due to the use of imprecise experimental paradigms and analytic methods that are ill-suited to disentangle the contribution of component processes to effort-cost decision-making, it is unknown whether this reduction is driven by reduced sensitivity to the rewards or heightened sensitivity to the effort associated with actions. This knowledge has treatment implications, as interventions for targeting reward and effort sensitivity are different. We will use a combination of experimental tasks and associated computational modeling approaches, to quantify the relative contributions of effort and reward sensitivity to effort-cost decision-making in people with schizophrenia and healthy controls. We will also collect mobile-based assessments providing comprehensive phenotyping of motivational impairment experienced in daily life. We aim (a) to determine whether effort-cost decision-making deficits in schizophrenia reflects increased effort or reduced reward sensitivity, (b) to identify the neural substrates of effort-cost decision-making impairment, and (c) to establish whether effort measures correspond to measures of effort and reward in daily life. The fact that few researchers have been trained in both clinical phenomenology and computational modeling techniques limits the translational impact these approaches may have in understanding mental illness. With this in mind, the training plan is specifically-designed to provide hands-on instruction in 1) applying computational models to effort-cost decision-making to choice behavior, 2) integrating computational modeling with functional neuroimaging, and 3) relating computational modeling parameters to mobile-based assessments of daily motivational experience. Taken together, completion of the proposal will facilitate the applicant’s long-term goal of becoming an independent investigator examining the computational mechanisms of motivational impairment in various psychiatric conditions. Further, the data and skills acquired will position the applicant to competitively submit a transdiagnostic R01 proposal, designed to examine whether effort-cost decision-making impairments in psychiatric conditions characterized by avolition (e.g., major depressive disorder, schizophrenia) arise from similar or different computational mechanisms.