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.

认知控制的障碍是许多精神健康障碍的核心（McTeague等，2017）。 相似地，来自网络的一系列神经影像学研究有越来越多的证据 心理健康疾病的计算（Fornito等，2015）。这两个关键的“失踪作品”桥接 大脑功能的各个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的关系以及这些关系波动的确切方式 并在每个人中展现了数周和几个月的时间。 在我们了解精神疾病轨迹中的波动之前，我们需要首先了解 随着时间的推移，健康个体的暂时变异性。 “获取的最近的'密集扫描'数据集 每个受试者的数据基本上为这一挑战提供了潜在的解决方案，但是这些研究缺乏 宽度（它们包括很少的主题，例如4-10）和广度（它们专注于单个任务/州，通常是 “休息状态”）。我们将通过一个数据集扫描55个受试者，总共12个主题来克服这些缺点 在6个月的整个过程中，在8个独特的任务中，这些工作涵盖了多个认知控制的多个构造 （工作记忆，注意力，设置转移，抑制和性能监控）。结果数据集将 要宽（即每个任务多个主题），广泛（例如，每个构造的多个任务）和深度（例如多个 随着时间的推移，每个任务的重复）。这种精确的神经科学方法使我们能够识别全球和本地 （a）为快速，有效控制的性能做准备的神经网络的变化， （b）支持灵活的发出后和冲突后控制调整以改善随后的 表现。一旦我们确定了认知控制的这些行为和神经网络签名， 在任务，构造，会话之间可重现，我们将在小说的目标网络中利用这些信息 通过对认知的精确挑战，攻击程序来设计网络体系结构中的分解 系统。量身定制的依赖重叠网络体系结构的任务组合将组合到 确定特定的网络功能，这些特征在健康受试者中成熟，因此代表了可能 随后疾病失败的节点。 这项工作将共同发现认知控制与功能性大脑网络之间的新颖联系 跨任务，构造和会议的架构（目标1）对于有效而灵活的行为至关重要 （AIM 2），并且很可能在潜水疾病状态下失败（AIM 3）。我们的精确神经科学方法与 由于我们的深层扫描程序使我们能够确定 主题级网络功能是有效的认知控制所必需的。另外，通过公开数据 其他研究人员可以访问，我们预计这些数据集将成为无与伦比的丰富来源 研究认知控制与网络级计算之间基本联系的人的信息。