Establishing Network Neuroscience Mechanisms of Efficiency of Evidence Accumulation in a Well-Characterized Sample with Bipolar Disorder: A Multi-Modal Clinical Imaging Study

摘要 双相情感障碍是一种严重的慢性疾病，人们对了解基于大脑的 导致紊乱症状的机制。在这份提案中，我们重点关注一位有希望的候选人： 证据积累效率(EEA)。EEA是用专门的模型来衡量的，来自计算 精神病学，它量化了在嘈杂的环境中从刺激中积累信息的基本神经认知能力 条件，以便选择适当的应对措施。在双相中发现EEA的显著减少 精神障碍，以及其他主要的精神障碍，导致冲动和疾病的严重性。 然而，在知识方面有一个关键的缺口：目前，我们对大脑机制知之甚少 在双相情感障碍或任何其他精神障碍中产生EEA减少。 在这个项目中，我们使用网络神经科学的方法来解决这一差距。大量证据来自 大型数据集强烈支持EEA灵活的网络重构模型。这个模型说的是EEA 取决于大脑自适应地重新配置大脑认知网络连接模式的能力 需求和任务上下文。该模型提出了减少双相情感障碍患者EEA的新假说 源于灵活的网络重新配置方面的不足。我们用密歇根大学的Unique检验了这一假说。 Prechter对双相情感障碍的纵向研究(由Co-I McInris领导)。我们研究了130名健康的成年人 130名患有双相情感障碍的成年人，他们完成了一系列行为任务来测量EEA和一系列 优化的神经成像任务，以测量大脑网络重新配置的灵活性。 我们方法的核心是使用大脑基集(BBS)，这是一种多变量预测模型 框架。这种方法使我们能够“总结”整个网络中成千上万的连接模式的变化 大脑在数量不多的基本重新配置组件方面。BBS让我们识别哪些网络 重新配置以及它们重新配置的数量。使用BBS，我们将量化大脑网络的重新配置 双相情感障碍的缺陷。此外，我们还发现EEA缺陷与大脑网络重构减少有关 特别是冲动性/情感不稳定的双相情感障碍亚型和冲动性因子得分。 最后，我们阐明了任务诱发的脑网络重构缺陷的原因。我们使用多变量 描述任务诱发的网络灵活性如何因大脑无任务状态的改变而降低的方法 功能和结构建筑。 EEA是一种计算指标，严格量化双相情感障碍的核心神经认知缺陷。这 该项目利用计算精神病学、网络神经科学和多模式成像来描绘大脑 支撑EEA的网络机制。这里的成功为更广泛的神经科学网络奠定了基础 一项研究计划，考察跨多个区域的大脑网络的重新配置/灵活性方面的损害 疾病，目的是查明病因和确定可能的干预措施。

Abstract Bipolar disorder is a serious chronic condition, and there is great interest in understanding brain-based mechanisms that contribute to disorder symptoms. In this proposal, we focus on one promising candidate: efficiency of evidence accumulation (EEA). EEA is measured in specialized models from computational psychiatry, and it quantifies a basic neurocognitive ability to accumulate information from a stimulus in noisy conditions in order to select appropriate responses. Substantial reductions in EEA are found in bipolar disorder, as well as other major psychiatric disorders, and they contribute to impulsivity and disease severity. There is a critical gap in knowledge, however: At the current time, we know little about the brain mechanism that produce reduced EEA in bipolar disorder, or in any other psychiatric disorder. In this project, we address this gap using the methods of network neuroscience. Substantial evidence from large datasets strongly supports a flexible network reconfiguration model of EEA. This model says EEA depends on the brain’s ability to adaptively reconfigure connectivity patterns of brain networks across cognitive demands and task contexts. The model suggests the novel hypothesis that reduced EEA in bipolar disorder arises from deficits in flexible network reconfiguration. We test this hypothesis with U. of Michigan’s unique Prechter Longitudinal Study of Bipolar Disorder (headed by Co-I McInnis). We study 130 healthy adults and 130 adults with bipolar disorder, who complete a battery of behavioral tasks to measure EEA and a battery of neuroimaging tasks optimized to measure flexibility of brain network reconfiguration. A centerpiece of our approach is the use of brain basis set (BBS), a multivariate predictive modeling framework. This method lets us “summarize” tens of thousands of changes in connectivity patterns across the brain in terms of a modest number of basic reconfiguration components. BBS lets us identify what networks reconfigure as well as how much they reconfigure. Using BBS, we will quantify brain network reconfiguration deficits in bipolar disorder. We in addition link deficits in EEA and reduced brain network reconfiguration specifically to an impulsive/affectively-unstable subtype of bipolar disorder and to impulsivity factor scores. Finally, we elucidate the etiology of deficits in task-evoked brain network reconfiguration. We use multivariate methods to delineate how reduced task-evoked network flexibility arises from alterations in the brain’s task-free functional and structural architecture. EEA is a computational metric that rigorously quantifies core neurocognitive deficits in bipolar disorder. This project leverages computational psychiatry, network neuroscience, and multi-modal imaging to delineate brain network mechanisms that underpin EEA. Success here lays the foundation for a broader network neuroscience research program examining impairments in reconfiguration/flexibility of brain networks across multiple disorders, with the aim of pinpointing etiology and identifying potential interventions.