NCS-FO: Modeling Individual Differences in Cognitive Control as Variation in Neural Activation Trajectories

NCS-FO：将认知控制的个体差异建模为神经激活轨迹的变化

• 批准号: 1835209
• 负责人: ShiNung Ching
• 金额: $ 61.06万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1835209&HistoricalAwards=false
• 关键词: NCS; FO; Modeling; Individual; Differences

NSF 1835209NCS-FO: Modeling Individual Differences in Cognitive Control as Variation in Neural Activation TrajectoriesAbstract:This award supports fundamental research to examine how activity within brain networks allows humans to adapt their behavior in order to achieve goals and complete mental tasks. Such processes within the brain, referred to as cognitive control, are thought to differentiate individuals in terms of mental abilities that are critical for successful navigation in activities of daily life, such as planning, problem solving and reasoning. Current brain imaging methods enable examination of the activity and interactions among brain networks as individuals perform various tasks, thus providing a window into the mechanisms of cognitive control. However, research efforts to date have mostly used imaging data to generate snapshots of brain activity that are averaged across groups of individuals and many different events while performing a task. In this research program, the investigators develop a new form of analysis to characterize the moment-to-moment fluctuations in brain activity, within each individual, as they transition from rest to cognitively demanding task conditions. In particular, efforts will be directed towards the development of a computational model that can predict how brain networks coordinate activity over seconds-level timescales in response to changing task conditions. A key aspect of the effort will be to develop unique models for each individual, drawing from a large database of previously obtained neuroimaging data. In these data, individuals perform a range of tasks requiring different cognitive control strategies, some proactive (sustained) versus others reactive (transient). Thus, application of the model will reveal how the brains of these individuals differentially respond to various types of cognitive demand. The development of this model also provides a unique opportunity for education and outreach; specific efforts will be directed toward the development of a software platform through which members of the public can work with demonstration models to probe and learn about how different patterns of brain activity relate to cognitive function.Functional neuroimaging has allowed for detailed spatial and temporal characterizations of brain network activation in an effort to elucidate the neural underpinnings of cognitive control. However, such analyses usually rely on static snapshots of neural activation patterns in individual brain regions and/or correlational indices of inter-regional co-activation (i.e., functional connectivity). Further progress in understanding distinctions between cognitive states and cognitive control strategies requires more precise descriptions of the brain dynamics that govern how patterns of neural activity (trajectories) evolve across time. Leveraging recent advancements in optimization theory that allow for reliable high-dimensional parameter estimation, this award will support the validation and parameterization of single-subject dynamical models using high-resolution, long-duration resting-state fMRI data from the Human Connectome Project, which contains data from over 1000 individuals. Subsequent model analysis will characterize individual differences in terms of brain network dynamics, focusing on quantitative metrics of the ruggedness of the attractor landscape (which indicates the diversity of achievable trajectories) and the consequent energetic costs incurred by shifting between cognitive states and strategies. Hypothesis testing will be conducted with a unique follow-up dataset, consisting of a subset of HCP participants and monozygotic (identical) twins (over 100 in total) tracked in multiple neuroimaging sessions, under conditions that systematically manipulate cognitive control strategies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF 1835209NCS-FO：将认知控制中的个体差异建模为神经激活轨迹的变化摘要：该奖项支持基础研究，以研究大脑网络内的活动如何使人类适应其行为，以实现目标并完成心理任务。 大脑中的这些过程被称为认知控制，被认为是在心理能力方面区分个体，这些心理能力对于在日常生活活动中成功导航至关重要，例如计划，解决问题和推理。 目前的脑成像方法能够检查个体执行各种任务时大脑网络之间的活动和相互作用，从而为认知控制机制提供了一个窗口。然而，迄今为止的研究工作主要使用成像数据来生成大脑活动的快照，这些快照是在执行任务时跨个体组和许多不同事件的平均值。在这项研究计划中，研究人员开发了一种新的分析形式，以描述每个人从休息过渡到认知要求高的任务条件时大脑活动的瞬间波动。 特别是，我们将努力开发一种计算模型，该模型可以预测大脑网络如何在秒级时间尺度上协调活动，以应对不断变化的任务条件。 这项工作的一个关键方面将是为每个人开发独特的模型，从以前获得的神经成像数据的大型数据库中提取。 在这些数据中，个体执行一系列需要不同认知控制策略的任务，一些是主动的（持续的），另一些是被动的（短暂的）。因此，该模型的应用将揭示这些个体的大脑如何对各种类型的认知需求做出不同的反应。 这一模式的发展还为教育和外联提供了一个独特的机会;我们将致力于开发一个软件平台，通过这个平台，公众可以使用演示模型来探索和了解大脑活动的不同模式与认知功能的关系。功能性神经成像可以详细描述大脑网络激活的空间和时间特征，阐明认知控制的神经基础。然而，这样的分析通常依赖于个体脑区域中的神经激活模式的静态快照和/或区域间共激活的相关指数（即，功能连接性）。要进一步理解认知状态和认知控制策略之间的区别，需要更精确地描述大脑动力学，这些动力学决定了神经活动（轨迹）的模式如何随时间演变。利用最优化理论的最新进展，允许可靠的高维参数估计，该奖项将支持使用来自人类连接组项目的高分辨率，长时间静息状态fMRI数据的单受试者动态模型的验证和参数化，该项目包含来自1000多个人的数据。随后的模型分析将从大脑网络动力学的角度来表征个体差异，重点关注吸引子景观的坚固性（这表明了可实现轨迹的多样性）以及由此产生的认知状态和策略之间转换所产生的能量成本的定量指标。将使用唯一的随访数据集进行假设检验，该数据集由HCP受试者和单合子受试者的子集组成。（同卵）双胞胎（总共超过100个）在多个神经成像会话中跟踪，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查进行评估，被认为值得支持的搜索.

项目成果

Resolving and characterizing the incidence of millihertz EEG modulation in critically ill children

解析和表征危重儿童毫赫兹脑电图调制的发生率

• DOI: 10.1016/j.clinph.2022.02.010
• 发表时间: 2022
• 期刊: Clinical Neurophysiology
• 影响因子: 4.7
• 作者: Loe, Maren E.;Khanmohammadi, Sina;Morrissey, Michael J.;Landre, Rebekah;Tomko, Stuart R.;Guerriero, Réjean M.;Ching, ShiNung
• 通讯作者: Ching, ShiNung

Efficient identification for modeling high-dimensional brain dynamics

高维大脑动力学建模的有效识别

• DOI: 10.23919/acc53348.2022.9867232
• 发表时间: 2022
• 期刊: 2022 American Control Conference (ACC
• 作者: Singh, Matthew F.;Wang, Michael;Cole, Michael W.;Ching, ShiNung
• 通讯作者: Ching, ShiNung

Detecting slow narrowband modulation in EEG signals

检测脑电图信号中的慢速窄带调制

• DOI: 10.1016/j.jneumeth.2022.109660
• 发表时间: 2022
• 期刊: Journal of Neuroscience Methods
• 影响因子: 3
• 作者: Loe, Maren E.;Morrissey, Michael J.;Tomko, Stuart R.;Guerriero, Réjean M.;Ching, ShiNung
• 通讯作者: Ching, ShiNung

Computing and Optimizing Over All Fixed-Points of Discrete Systems on Large Networks

• DOI: 10.1098/rsif.2020.0126
• 发表时间: 2020-03
• 期刊: bioRxiv
• 作者: James R. Riehl;Maxwell I. Zimmerman;Matthew F. Singh;G. Bowman;ShiNung Ching