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.

血液氧合水平依赖（粗体）磁共振成像（MRI）的波动 映射功能连接性，其中包含有关神经活动和物理过程的大量信息 在大脑中。大多数功能连通性研究希望检测与认知和认知和 信息处理，并将其他贡献者视为其他贡献者 并发症。但是，有证据表明，在包含临床上包含的粗体信号中“噪声”来源 有关不同空间和临时量表活动活动的相关信息。挑战在于分离 来自不同过程的贡献，以便可以实现对感兴趣过程的选择性敏感性。 我们建议将空间，光谱和临时信号特性与多模式成像结合到 将粗体波动分为具有不同空间和临时量表的四个组件：1）a 与源性电活动相关的准静脉时空图案（QPP）； 2）振荡来自 脉管系统的特性； 3）不反映局部神经处理的全球信号变化； 4） 剩余的可变性，这应该增加对区域之间时间变化相互作用的敏感性。 使粗体组件隔离的两个关键要素是直接测量 神经活动与大鼠模型中的成像实验结合，以及动态分析技术 可以在成像和记录数据中捕获空间和临时模式。而基础工作 该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 在人类中的标准静止状态功能MRI（RS-FMRI）研究。 我们的初步数据表明，BOLD信号包含两种单独类型的贡献 神经活动：基源性活性，产生大胆激活的准时代时空模式； 和典型的脑电带的活动，这与区域之间的时变活动紧密相关。仅使用 分析工具，我们表明我们可以分开并确定人类数据中的类似过程，这是一个有力的论点 这些技术的最终可翻译性。我们还表明，仅QPPS是 重大抑郁症和健康对照患者之间观察到的连通性差异， 这证明了选择性分析方法如何有助于诊断精神病和神经系统 疾病并提供有关许多疾病所表现出的连通性改变的新见解。我们展出 神经和血管对全球粗体信号的贡献的初步证据，并描述 绘制血管振荡贡献的方法。具体目的是：1。确定神经和 全局粗体信号的血液动力学相关性； 2。表征血管振荡的贡献； 3。区分带有限制性的贡献与1/Fβ活性的粗体相关性； 4。将发现转化为人类 研究。