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

在充分表征的双相情感障碍样本中建立证据积累效率的网络神经科学机制：多模式临床影像研究

• 批准号: 10628028
• 负责人: Chandra Sekhar Sripada
• 金额: $ 75.06万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10628028
• 关键词: Address; Adult; Affect; Affective; Age; Architecture; Bayesian Analysis; Behavioral; Bipolar Disorder; Brain; Chronic; Clinical; Cognition; Cognitive; Computer Models; Data; Development; Diagnosis; Diffusion; Dimensions; Disease; Etiology; Fostering; Foundations; Functional Magnetic Resonance Imaging; Genetic; Impaired cognition; Impairment; Impulsivity; Individual; Individual Differences; Intervention; Knowledge; Link; Longitudinal Studies; Measures; Mediating; Mediation; Mental disorders; Methodology; Methods; Michigan; Modeling; Multimodal Imaging; Neurocognitive; Neurocognitive Deficit; Neurosciences; Neurosciences Research; Participant; Pattern; Performance; Pilot Projects; Population; Psychiatry; Psychopathology; Publishing; Regulation; Rest; Sampling; Severity of illness; Stimulus; Symptoms; Task Performances; Testing; Time; Translations; Work; brain based; clinical imaging; cognitive ability; cognitive training; connectome; disability; flexibility; follow-up; imaging study; improved; interest; large datasets; longitudinal course; mathematical model; multimodality; network architecture; neuroimaging; neuromechanism; novel; predictive modeling; programs; response; success; trait; trait impulsivity

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.

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，我们专注于一个有希望的候选人： 证据积累效率（EEA）。EEA是在计算的专门模型中测量的。 它量化了一种基本的神经认知能力，即从嘈杂的环境中的刺激中积累信息。 条件，以选择适当的反应。在双极患者中发现EEA显著降低 精神障碍，以及其他主要的精神疾病，他们有助于冲动和疾病的严重性。 然而，在知识上存在着一个关键的差距：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疾病中产生减少的EEA。 在这个项目中，我们使用网络神经科学的方法来解决这个差距。大量证据表明， 大数据集强有力地支持EEA灵活的网络重构模型。这个模型说EEA 依赖于大脑的能力，以适应性地重新配置大脑网络的连接模式， 需求和任务上下文。该模型提出了双相情感障碍患者EEA减少的新假设 这是由于灵活的网络重新配置的不足。我们用U检验这个假设。密歇根州独特的 双相情感障碍的Prechter纵向研究（由Co-I McInnis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 神经成像任务被优化以测量大脑网络重构的灵活性。 我们的方法的核心是使用脑基集（BBS），一个多变量预测模型 框架.这种方法可以让我们"总结"整个网络中连接模式的数万个变化， 大脑中的一个适度数量的基本重新配置组件。BBS让我们识别哪些网络 重新配置以及它们重新配置的程度。使用BBS，我们将量化大脑网络重构 双相情感障碍的缺陷此外，我们还将EEA的缺陷与大脑网络重构的减少联系起来， 特别是双相情感障碍的冲动/情感不稳定亚型和冲动因子得分。 最后，我们阐明了任务诱发脑网络重构缺陷的病因。我们使用多变量 描述任务诱发网络灵活性降低是如何从大脑无任务功能的改变中产生的方法。 功能和结构体系结构。 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 网络机制是EEA的基础。这里的成功为更广泛的网络神经科学奠定了基础 一项研究计划，检查大脑网络在多个领域的重新配置/灵活性方面的损伤 疾病，目的是查明病因和确定潜在的干预措施。