Macroscale physiology and functional correlates of slow network fluctuations

摘要。行为、认知和神经状态的缓慢波动是神经系统的一个无所不在的特征。 大脑跨物种，并在神经系统的多种疾病异常。慢脑网络 波动在延长的时间尺度上严格地组织行为：就像唤醒在几分钟内漂移一样， 因为我们能够在外部参与和内部参与之间切换，以计划我们未来的行动， 详述最近的经历。尽管大脑网络的缓慢波动对行为的影响 通常通过将一个网络测量与一种行为联系起来进行研究，该项目旨在更广泛地 理解健康人类参与者的缓慢波动，i）深入描述他们之间的关系， 同时测量大脑活动、生理、行为和认知的多种测量，以及ii） 因果地操纵认知控制和唤醒的关键因素，假设这些因素是为了协调 缓慢的网络波动和持续的行为之间的关系或模式之间的切换 外部和内部导向的注意力。为了实现这些目标，Aim 1将进行深入的 通过收集广泛的多模态神经和生理特征来表征缓慢的网络波动 健康人类参与者的记录。同步功能磁共振成像脑电图皮肤电活动瞳孔测量 呼吸，心电图和肌电图将被记录为参与者执行一系列扩展的任务， 非结构化（休息）到高度结构化的注意力要求任务。经验取样将使我们能够评估 持续认知的内容。这个丰富的数据集将使我们能够以前所未有的方式，测量和 将多种模式之间的缓慢波动联系起来，将它们与外部导向的行为之间的关系映射出来， 任务和内部认知的内容。虽然目标1将审查缓慢波动与 在刺激驱动的任务中，我们将注意力集中在内部的能力可能具有适应性益处， 在基于外部的实验室任务中通常不会捕获的功能。目标2a将填补这一空白， 评估缓慢的大脑网络波动对支持内部处理的贡献， 我们后来的行为。具体来说，我们将测量与学习和目标相关的大脑模式 计划任务，并评估这些任务的持续内部处理的神经和行为标记 在随后的时间段内。这将使我们能够直接将缓慢的网络波动与收益联系起来 与内部导向的认知相关，并衡量内部与外部之间的权衡， 认知模式导向。另一个主要问题涉及对缓慢网络波动的监管。目标2b 将评估两个关键因素的潜在因果关系，认知控制和唤醒，以减缓 波动及其对行为和认知的影响。这些因素将独立 通过任务要求和双盲药物管理来评估其独特的贡献。 这些实验将极大地推进我们对大脑网络缓慢波动在大脑活动中的作用的理解。 协调适应性行为和认知。

ABSTRACT. Slow fluctuations in behavioral, cognitive, and neural states are an omnipresent feature of the brain across species and are aberrant in multiple disorders of the nervous system. Slow brain network fluctuations critically organize behavior across extended timescales: as drifts in arousal over minutes, as well as our ability to switch between attending externally versus turning internally to plan our future actions and dwell on recent experiences. Although the contribution of slow brain network fluctuations to behavior have typically been studied by linking one network measure to one behavior, this project seeks to more broadly understand slow fluctuations in healthy human participants by i) deeply characterizing their relationship across multiple measures of brain activity, physiology, behavior, and cognition measured simultaneously and ii) causally manipulating key factors of cognitive control and arousal which are hypothesized to orchestrate relationships between slow network fluctuations and ongoing behavior or switching between modes of externally versus internally oriented attention. To achieve these goals, Aim 1 will perform a deep characterization of slow network fluctuations by collecting extensive multimodal neural and physiological recordings in healthy human participants. Simultaneous fMRI, EEG, electrodermal activity, pupillometry, respiration, ECG, and EMG will be recorded as participants perform an extended array of tasks ranging from unstructured (rest) to highly structured attention-demanding tasks. Experience sampling will allow us to assess the contents of ongoing cognition. This rich dataset will allow us to, in an unprecedented manner, measure and link slow fluctuations across multiple modalities, map their relationship with behavior on externally-oriented tasks and to the contents of internal cognition. While Aim 1 will examine the relevance of slow fluctuations to performance on stimulus- driven tasks, our ability to direct attention internally likely has adaptive benefits, a feature not typically captured in externally-oriented lab-based tasks. Aim 2a will fill in this gap by directly assessing the contributions of slow brain network fluctuations to supporting internal processing that benefits our subsequent behavior. Specifically we will measure brain patterns associated with learning and goal planning tasks, and assess neural and behavioral markers of continued internal processing on these tasks during a subsequent time period. This will allow us to directly link slow network fluctuations to benefits associated with internally-oriented cognition, and measure trade-offs between internally- versus externally- oriented modes of cognition. Another major question relates to regulation of slow network fluctuations. Aim 2b will assess the potential causal contributions of two key factors, cognitive control and arousal, to slow fluctuations and their resulting impact on behavior and cognition. These factors will be independently manipulated via task demands and double-blind drug administration to assess their distinct contributions. These experiments will dramatically advance our understanding of the role of slow brain network fluctuations in orchestrating adaptive behavior and cognition.