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: 10398085
• 负责人: Russell A Poldrack
• 金额: $ 33.97万
• 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 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10398085
• 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.

认知控制的损伤是许多精神健康障碍的核心（McTeague等人，2017年）。在 与此同时，越来越多的神经影像学研究表明， 心理健康障碍中的计算（Fornito等人，2015年）。一个关键的“缺失的一块”连接这两个 大脑功能的方面是对网络级计算的方式的理解相对较差 与认知控制过程有关，以及这些关系波动的精确方式 并在每个个体身上持续数周或数月。 在我们理解精神疾病发展轨迹的波动之前，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， 每个受试者的数据要多得多，这为这一挑战提供了潜在的解决方案，但这些研究缺乏 宽度（它们包括很少的主题，例如，4-10）和广度（他们专注于个别任务/状态，通常是 “静止状态”）。我们将用一个数据集来克服这些缺点，每个数据集扫描55个主题，总共12个 在6个月的时间里，他们在8个独特的任务上花费了10个小时，这些任务跨越了认知控制的多种结构。 （工作记忆、注意力、定势转换、抑制和性能监控）。生成的数据集将 要宽（即每个任务多个主题）、宽（例如每个结构多个任务）和深（例如多个 每一个任务的重复时间）。这种精确的神经科学方法使我们能够识别全局和局部 神经网络的变化是必要的，（a）为快速，有效的控制性能做准备， 以及（B）支持灵活的错误后和冲突后控制调整，以改善后续的 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，会话可重复的，我们将在一个新的“目标网络”中利用这些信息 攻击的过程，以工程师的故障，在网络体系结构的精度挑战的认知 系统依赖于重叠网络架构的任务的定制组合将被组合， 识别在健康受试者中成熟失效的特定网络特征，因此，代表可能的 疾病的后续失败的节点。 总之，这项工作将揭示认知控制和功能性大脑网络之间的新联系 跨任务、构造和会话的架构（目标1），这对于有效和灵活的行为至关重要 (Aim 2）并且可能在不同的疾病状态下失败（目标3）。我们精确的神经科学方法 与NIH的精准医学计划密切相关，因为我们的深度扫描程序可以让我们识别 有效认知控制所需的主体级网络功能。此外，通过公开数据， 我们希望这些数据集将成为一个无比丰富的信息来源， 为那些研究认知控制和网络级计算之间的重要联系的人提供信息。

项目成果

Severe violations of independence in response inhibition tasks.

• DOI: 10.1126/sciadv.abf4355
• 发表时间: 2021-03
• 期刊: Science advances
• 影响因子: 13.6
• 作者: Bissett PG;Jones HM;Poldrack RA;Logan GD
• 通讯作者: Logan GD

A dual-task approach to inform the taxonomy of inhibition-related processes.

一种双任务方法，用于告知抑制相关过程的分类。

• DOI: 10.1037/xhp0001073
• 发表时间: 2023
• 期刊: Journal of experimental psychology. Human perception and performance
• 作者: Bissett,PatrickG;Jones,HenryM;Hagen,McKenzieP;Bui,TungT;Li,JamieK;Rios,JaimeAliH;Mumford,JeanetteA;Shine,JamesM;Poldrack,RussellA
• 通讯作者: Poldrack,RussellA

A multi-sample evaluation of the measurement structure and function of the modified monetary incentive delay task in adolescents.

• DOI: 10.1016/j.dcn.2023.101337
• 发表时间: 2024-02
• 期刊: DEVELOPMENTAL COGNITIVE NEUROSCIENCE
• 影响因子: 4.7
• 作者: Demidenko, Michael I.;Mumford, Jeanette A.;Ram, Nilam;Poldrack, Russell A.
• 通讯作者: Poldrack, Russell A.

Open exploration.

开放探索。

• DOI: 10.7554/elife.52157
• 发表时间: 2020
• 期刊: eLife
• 影响因子: 7.7
• 作者: Thompson,WilliamHedley;Wright,Jessey;Bissett,PatrickG
• 通讯作者: Bissett,PatrickG