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: 9205825
• 负责人: Shella D Keilholz
• 金额: $ 38.44万
• 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/9205825
• 关键词: Accounting; Affect; Area; Brain; Characteristics; Clinical Research; Cognition; Communication; Complication; Data; Diagnosis; Disease; Electrodes; Electroencephalography; Elements; Excision; Exhibits; 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; Rattus; Rest; Signal Transduction; Source; Structure; Techniques; Time; Translating; Translations; Variant; Work; analytical tool; base; blood oxygen level dependent; brain dysfunction; cerebral blood volume; clinically relevant; diagnosis evaluation; hemodynamics; human data; human subject; implantation; improved; information processing; insight; interest; nervous system disorder; neurophysiology; relating to nervous system; research study; response; spatiotemporal; tool; 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.

血氧水平依赖性（BOLD）磁共振成像（MRI）波动用于 地图功能连接包含了丰富的信息，神经活动和生理过程 在大脑中。大多数功能连接研究希望检测与认知相关的时变活动， 信息处理，并认为存在的其他贡献者的自发BOLD波动作为一个 并发症然而，越来越多的证据表明，BOLD信号中的“噪声”来源包含临床- 有关不同空间和时间尺度活动的相关信息。挑战在于将 因此，可以从不同的过程中获得不同的贡献，从而可以实现对感兴趣的过程的选择性灵敏度。 我们建议将联合收割机空间、光谱和时间信号特征与多模态成像相结合， 将BOLD波动分成具有不同空间和时间尺度的四个分量：1）a 准周期时空模式（QPP）与次声电活动; 2）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）血管系统的特性。 这将增加对区域间随时间变化的相互作用的敏感性。 使BOLD组件隔离成为可能的两个关键因素是直接测量 结合大鼠模型中的成像实验的神经活动，以及动态分析技术， 可以捕获成像和记录数据中的空间和时间模式。虽然基础工作 本提案中描述的将在大鼠中执行，我们开发的工具将被优化并应用于 在人类中进行的标准静息状态功能性MRI（rs-fMRI）研究。 我们的初步数据表明，BOLD信号包含两种可分离类型的贡献， 神经活动：次声活动，产生BOLD激活的准周期性时空模式; 以及典型EEG频带中的活动，其与区域之间的时变活动更紧密地联系在一起。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据， 这些技术的最终可译性。我们还表明，QPP单独占 在重度抑郁症患者和健康对照组之间观察到的连通性差异， 它展示了选择性分析方法如何帮助诊断精神和神经疾病， 并提供了新的见解，在连接的变化，许多疾病的表现。我们表现出 初步证据的神经和血管的贡献，全球BOLD信号，并描述了一个 方法映射血管振荡的贡献。具体目的是：1.确定神经和 全球BOLD信号的血流动力学相关性; 2.描述血管振荡的贡献; 3.区分带限贡献与1/fβ活性的BOLD相关性; 4.将发现转化为人类 问题研究