Spatiotemporal signatures of neural activity and neurophysiology in the BOLD signal

BOLD 信号中神经活动和神经生理学的时空特征

• 批准号: 9754248
• 负责人: Shella D Keilholz
• 金额: $ 38.32万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-14 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9754248
• 关键词: Affect; Area; Brain; Characteristics; Clinical Research; Cognition; Communication; Complication; Data; Disease; Electrodes; Electroencephalography; Elements; Excision; Exhibits; Foundations; Frequencies; Functional Magnetic Resonance Imaging; Human; Image; Link; Magnetic Resonance Imaging; Major Depressive Disorder; Maps; Measurement; Mediating; Mental disorders; Methods; Modeling; Multimodal Imaging; Neurosciences; Noise; Patients; Pattern; Physiological Processes; Process; Property; Psychiatric Diagnosis; Rattus; Rest; Signal Transduction; Source; Structure; Techniques; Time; Translating; Variant; Work; analytical tool; blood oxygen level dependent; brain dysfunction; cerebral blood volume; clinically relevant; diagnosis evaluation; experimental study; hemodynamics; human data; human subject; implantation; improved; information processing; insight; interest; nervous system disorder; neurophysiology; relating to nervous system; response; spatiotemporal; tool; translation to humans; vascular contributions

The blood oxygenation level dependent (BOLD) magnetic resonance imaging (MRI) fluctuations used to map functional connectivity contain a wealth of information about neural activity and physiological processes in the brain. Most functional connectivity studies wish to detect time-varying activity related to cognition and information processing, and view the presence of other contributors to the spontaneous BOLD fluctuations as a complication. However, evidence is growing that sources of “noise” in the BOLD signal contain clinically- relevant information about activity at different spatial and temporal scales. The challenge lies in separating contributions from different processes so that selective sensitivity to the process of interest can be achieved. We propose to combine spatial, spectral and temporal signal characteristics with multi-modal imaging to separate the BOLD fluctuations into four components with different spatial and temporal scales: 1) a quasiperiodic spatiotemporal pattern (QPP) linked to infraslow electrical activity; 2) oscillations that arise from properties of the vasculature; 3) global signal variations that do not reflect local neural processing; and 4) the remaining variability, which should have increased sensitivity to time-varying interactions between regions. The two key elements that make the isolation of BOLD components possible are the direct measurement of neural activity in conjunction with imaging experiments in the rat model, and dynamic analysis techniques that can capture spatial and temporal patterns in the imaging and recording data. While the foundational work described in this proposal will be performed in the rat, the tools we develop will be optimized and applied to standard resting state functional MRI (rs-fMRI) studies in humans. Our preliminary data shows that the BOLD signal contains contributions from two separable types of neural activity: infraslow activity, which produces quasiperiodic spatiotemporal patterns of BOLD activation; and activity in typical EEG bands, which is more closely tied to time-varying activity between areas. Using only analytical tools, we show that we can separate and identify similar processes in human data, a strong argument for the ultimate translatability of these techniques. We also show that the QPPs alone account for the differences in connectivity observed between patients with major depressive disorder and healthy controls, which demonstrates how selective analysis methods can aid in the diagnosis of psychiatric and neurological disorders and provide new insight into the alterations in connectivity that many disorders exhibit. We exhibit preliminary evidence for both neural and vascular contributions to the global BOLD signal, and describe a method for mapping the contribution of vascular oscillations. Specific aims are: 1.Determine the neural and hemodynamic correlates of the global BOLD signal; 2. Characterize the contributions of vascular oscillations; 3.Distinguish bandlimited contributions from BOLD correlates of 1/fβ activity; 4. Translate findings to human studies.

血氧水平依赖(BOLD)磁共振成像(MRI)波动用于 MAP功能连通性包含有关神经活动和生理过程的丰富信息 在大脑里。大多数功能连接性研究都希望检测与认知相关的时变活动 信息处理，并将导致自发剧烈波动的其他因素的存在视为 很复杂。然而，越来越多的证据表明，大胆信号中的“噪音”来源在临床上包含- 关于不同空间和时间尺度上的活动的相关信息。挑战在于分离 来自不同过程的贡献，从而能够实现对感兴趣的过程的选择性敏感性。 我们建议将空间、光谱和时间信号特征与多模式成像相结合，以 将剧烈波动分成四个不同时空尺度的分量：1)a) 与水下电活动有关的准周期时空模式(QPP)；2)由以下因素引起的振荡 血管系统的特性；3)不反映局部神经处理的全局信号变化；以及4) 剩余的可变性，这应该会增加对区域之间随时间变化的相互作用的敏感性。 使粗体组件隔离成为可能的两个关键元素是 大鼠模型中神经活动与成像实验的结合，以及动态分析技术 可以捕捉成像和记录数据中的空间和时间模式。而基础性的工作 该方案将在RAT中执行，我们开发的工具将进行优化并应用于 人类标准静息功能核磁共振(rs-fmri)研究。 我们的初步数据显示，粗体信号包含来自两种可分离类型的 神经活动：下流活动，产生大胆激活的准周期时空模式； 以及典型脑电频段的活动，这与区域之间的时变活动更密切相关。仅使用 分析工具，我们表明，我们可以分离和识别人类数据中的类似过程，这是一个强有力的论点 这些技术的终极可译性。我们还表明，仅QPP一项就可以解释 在重度抑郁障碍患者和健康对照组之间观察到的连接性差异， 这证明了选择性分析方法如何有助于精神病学和神经学的诊断 并为许多疾病表现出的连接性变化提供了新的见解。我们展示了 初步证据表明神经和血管对全球BOLD信号的贡献，并描述了 绘制血管振荡贡献图的方法。具体目标是：1.确定神经和 血流动力学与整体BOLD信号的相关性；2.表征血管振荡的贡献； 3.区分1/fβ活性的带限贡献和粗体相关；4.将研究结果翻译给人类 学习。