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

NSF 1835209NCS-FO：将认知控制中的个体差异建模为神经激活训练中的变量摘要：该奖项支持基础研究，以检查大脑网络中的活动如何允许人类调整其行为，以实现目标和完成脑力任务。大脑中的这种被称为认知控制的过程被认为在心理能力方面区分个人，这些能力对于在日常生活活动中成功导航至关重要，如计划、解决问题和推理。目前的脑成像方法能够检查个体在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，到目前为止，研究工作主要是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群体和许多不同事件平均计算的。在这项研究计划中，研究人员开发了一种新的分析形式，以表征每个人从休息到认知要求高的任务条件时，大脑活动的时刻波动。特别是，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别的时间尺度上协调活动，以响应不断变化的任务条件。这项工作的一个关键方面将是从先前获得的神经成像数据的大型数据库中提取数据，为每个人开发独特的模型。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(瞬时的)。因此，该模型的应用将揭示这些人的大脑如何对各种类型的认知需求做出不同的反应。这一模型的开发还为教育和推广提供了一个独特的机会；具体的努力将针对开发一个软件平台，通过该平台，公众可以使用演示模型来探索和了解不同的大脑活动模式如何与认知功能相关。功能神经成像允许详细描述大脑网络激活的空间和时间特征，以努力阐明认知控制的神经基础。然而，这种分析通常依赖于单个脑区神经激活模式的静态快照和/或区域间共同激活(即功能连接)的相关指数。要进一步理解认知状态和认知控制策略之间的区别，就需要更准确地描述控制神经活动模式(轨迹)如何随时间演变的大脑动力学。利用优化理论的最新进展，允许可靠的高维参数估计，该奖项将支持使用人类连接项目的高分辨率、长时间静息状态fMRI数据对单对象动态模型进行验证和参数化，该数据包含来自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表征个体差异，重点放在吸引器场景的粗犷程度(指示可实现轨迹的多样性)以及认知状态和策略之间的转换所产生的能量成本的量化度量上。假设检验将使用一个独特的后续数据集进行，该数据集由多个神经成像会话中跟踪的HCP参与者和同卵双胞胎(总共超过100个)的子集组成，在系统操纵认知控制策略的条件下进行。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.