Characterizing cognitive control networks using a precision neuroscience approach

使用精确神经科学方法表征认知控制网络

• 批准号: 9906911
• 负责人: Russell A Poldrack
• 金额: $ 45.64万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9906911
• 关键词: Attention; Behavior; Behavioral; Behavioral trial; Brain; Conflict (Psychology); Data; Data Set; Disease; Engineering; Failure; Functional Magnetic Resonance Imaging; Head Movements; Hour; Impairment; Individual; Joints; Link; Mental Health; Mental disorders; Methodology; Monitor; Neurosciences; Patients; Performance; Pharmaceutical Preparations; Precision Medicine Initiative; Preparation; Procedures; Process; Reproducibility; Research Personnel; Rest; Sample Size; Scanning; Short-Term Memory; Source; Structure; Task Performances; Time; United States National Institutes of Health; Width; Work; base; cognitive control; cognitive system; comorbidity; experience; flexibility; functional MRI scan; improved; network architecture; network attack; neural network; neuroimaging; novel; open data

Impairments in cognitive control are central to many mental health disorders (McTeague et al., 2017). In parallel, there is mounting evidence from a range of neuroimaging studies implicating impairments of network computations in disorders of mental health (Fornito et al., 2015). A crucial ‘missing piece’ bridging these two aspects of brain function is a relatively poor understanding of the way in which the network-level computations of the brain relate to cognitive control processes, and the precise ways in which these relationships fluctuate and unfold over weeks and months in each individual. Before we can understand fluctuations in the trajectories of mental illnesses, we need to first understand the temporal variability of healthy individuals over time. “Recent ‘dense-scanning’ datasets that acquire substantially more data per subject provide a potential solution to this challenge, but these studies have lacked width (they include few subjects, e.g., 4-10) and breadth (they focus on individual tasks/states, often the ‘resting state’). We will overcome these shortcoming with a dataset scanning 55 subjects each for a total 12 hours over the course of 6 months on 8 unique tasks that span multiple constructs of cognitive control (working memory, attention, set shifting, inhibition, and performance monitoring). The resultant dataset will be wide (i.e. multiple subjects per task), broad (e.g. multiple tasks per construct) and deep (e.g. multiple repetitions of each task over time). This precision neuroscience approach allows us to identify global and local changes in neural networks that are necessary both (a) in preparation for fast, effective controlled performance, and (b) to support flexible post-error and post-conflict control adjustments to improve subsequent performance. Once we have identified these behavioral and neural network signatures of cognitive control that are reproducible across task, construct, session, we will leverage this information in a novel ‘targeted network attack’ procedure to engineer breakdowns in the network architecture by precision challenges to the cognitive system. Tailored combinations of tasks that rely on overlapping network architectures will be combined to identify specific network features that are ripe for failure in healthy subjects, and as such, represent likely nodes for subsequent failure in disease. Together, this work will uncover novel links between cognitive control and functional brain network architecture across tasks, constructs, and sessions (Aim 1) that are essential for effective and flexible behavior (Aim 2) and are likely to fail across diverse disease states (Aim 3). Our precision neuroscience approach relates closely to the precision medicine initiative at the NIH, as our deep-scanning procedure allows us to identify subject-level network features necessary for effective cognitive control. In addition, by making the data openly accessible to other researchers, we expect these data sets will become an incomparably rich source of information for those studying the essential link between cognitive control and network-level computations.

认知控制障碍是许多精神健康障碍的核心(McTeague等人，2017年)。在……里面 与此同时，来自一系列神经成像研究的越来越多的证据表明，网络损伤 精神健康障碍的计算(Fornito等人，2015年)。连接这两个人的一个关键的“缺失的部分” 大脑功能方面是对网络级计算方式的相对较差的理解 大脑与认知控制过程有关，以及这些关系波动的确切方式 并在每个个体身上展开数周和数月的过程。 在我们能够理解精神疾病轨迹的波动之前，我们需要首先了解 健康个体随时间的时间变异性。“最近的‘密集扫描’数据集 每个受试者更多的数据为这一挑战提供了潜在的解决方案，但这些研究一直缺乏 宽度(它们包括很少的主题，例如，4-10)和宽度(它们侧重于单个任务/状态，通常是 “休眠状态”)。我们将用一个数据集来克服这些缺点，每个数据集扫描55个对象，总共12个 在6个月的时间里完成8项独特的任务，这些任务跨越了认知控制的多种结构 (工作记忆、注意力、定势转移、抑制和性能监控)。生成的数据集将 广度(即每个任务有多个主题)、广度(例如每个构思有多个任务)和深度(例如多个 每项任务随时间的重复)。这种精确的神经科学方法使我们能够识别全球和局部 神经网络的改变是必要的：(A)为快速、有效的受控性能做准备， 和(B)支持灵活的错误后和冲突后控制调整，以改善后续工作 性能。一旦我们确定了这些认知控制的行为和神经网络特征 是跨任务、构造、会话可重现的，我们将在一个新颖的目标网络中利用这些信息 通过对认知的精确性挑战来设计网络架构故障的攻击程序 系统。依赖重叠网络体系结构的定制任务组合将组合在一起，以 确定在健康受试者中出现故障的特定网络功能，并因此表示可能 节点，为疾病的后续失效做准备。 总而言之，这项工作将揭示认知控制和大脑功能网络之间的新联系 跨任务、构造和会话的体系结构(目标1)，这对于有效和灵活的行为至关重要 (目标2)，而且很可能在不同的疾病状态下失败(目标3)。我们的精确神经科学方法与 与NIH的精准医学计划密切相关，因为我们的深度扫描程序允许我们识别 受试者级别的网络功能是有效认知控制所必需的。此外，通过将数据公开 对于其他研究人员来说，我们预计这些数据集将成为无比丰富的 为那些研究认知控制和网络级计算之间的基本联系的人提供信息。