Computational Mechanisms of Effort-Cost Decision-Making in Schizophrenia

精神分裂症的努力成本决策的计算机制

• 批准号: 10425423
• 负责人: ADAM J. CULBRETH
• 金额: $ 16.55万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10425423
• 关键词: Address; Anterior; Behavior; Behavioral; Brain; Choice Behavior; Clinical; Complex; Computer Models; Computing Methodologies; Data; Decision Making; Equation; Etiology; Functional Magnetic Resonance Imaging; Goals; Impairment; Intervention; Knowledge; Life; Link; Magnetic Resonance Imaging; Major Depressive Disorder; Mathematics; Measures; Mental disorders; Mind; Modeling; Motivation; National Institute of Mental Health; Occupational; Patient Self-Report; Pattern; Persons; Phenotype; Positioning Attribute; Prefrontal Cortex; Process; Public Health; Quality of life; Research; Research Personnel; Rewards; Schizophrenia; Severities; Specific qualifier value; Specificity; Techniques; Technology; Training; Training Technics; Ventral Striatum; Work; analytical method; base; behavior measurement; cingulate cortex; cost; design; experience; functional MRI scan; hands on instruction; mobile application; mobile computing; neural correlate; neuroimaging; neuromechanism; novel; phenomenological models; psychologic; relating to nervous system; response; skills; smartphone based assessment; tool; translational impact

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.

项目概要： 许多精神分裂症患者的动机减少，这损害了职业健康。 降低生活质量，增加公共卫生需求。治疗动机 然而，SZ中的损伤在很大程度上是无效的，部分原因是对病因学的理解不足。最近的工作 已经提供了强有力的证据表明，不正常的努力成本决策计算进行权衡， 行动的“成本与收益”-可能是精神分裂症中动机缺陷的关键因素。具体地说， 研究表明，精神分裂症患者比对照组更不愿意花费精力来获得 实验任务的奖励，这种赤字与动机障碍有关。但由于 使用不精确的实验范式和分析方法，不适合解开 由于组件过程对努力成本决策的贡献，目前还不清楚这种减少是否是 由对奖励的敏感性降低或对与行动相关的努力的敏感性提高驱动。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不同。 我们将结合实验任务和相关的计算建模方法， 量化的相对贡献的努力和奖励敏感性的努力成本决策的人， 精神分裂症和健康对照。我们还将收集基于移动设备的评估， 日常生活中经历的动机障碍的表型。我们的目标是（a）确定是否努力成本 精神分裂症中的决策缺陷反映了努力增加或奖励敏感性降低，（B）确定 努力成本决策障碍的神经基质，以及（c）确定努力是否衡量 与日常生活中的努力和回报相对应。 事实上，很少有研究人员在临床现象学和计算方面都受过训练， 建模技术限制了这些方法在理解心理学中可能产生的翻译影响， 病考虑到这一点，培训计划是专门设计的，以提供实践指导1）应用 计算模型到努力成本决策到选择行为，2）整合计算模型 与功能性神经成像，以及3）将计算建模参数与基于移动的 日常激励经验的评估。总的来说，完成这项建议将有助于 申请人的长期目标是成为一个独立的研究人员检查计算机制 在各种精神疾病中的动机障碍。此外，获得的数据和技能将定位 申请人竞争性地提交了一份跨诊断R 01提案，旨在检查是否努力-成本 以无意志为特征的精神病状态中的决策障碍（例如，重度抑郁 精神分裂症）源于相似或不同的计算机制。