人脑血氧水平依赖波动的多频段功能研究
文献类型：学位论文
| 作者 | 宫竹青 |
| 答辩日期 | 2022-06 |
| 文献子类 | 博士 |
| 授予单位 | 中国科学院大学 |
| 授予地点 | 中国科学院心理研究所 |
| 其他责任者 | 左西年 |
| 关键词 | 多频段功能磁共振分析 血氧水平依赖波动 低频波动 脑发育 |
| 学位名称 | 理学博士 |
| 学位专业 | 认知神经科学 |
| 其他题名 | Multi-band frequency functional analysis of blood-oxygenation-level- dependent oscillations in human brain |
| 中文摘要 | The brain is the most important and complex structure in the nervous system. Decoding how the brain works is the ultimate goal of neuroscientists. Neurons in the brain transmit and process information by generating action potentials. These microscopic electrical signals can be measured directly or indirectly by different techniques, generating neural oscillations at different levels. Single-unit recording, for example, can directly measure the electrical fluctuations of neurons at the microscopic level. Electroencephalography (EEG) measures brain oscillations generated by the electrical activity of millions of neurons at a macro level through extracranial electrodes. Functional magnetic resonance imaging (fMRI) can indirectly measure neural oscillations by measuring changes of the blood-oxygenation-level-dependent (BOLD) oscillations in cerebral blood flow. Over the past century, researches on human brain function using techniques such as EEG have found that different brain functions and physiological or psychological states prefer different oscillation frequencies. However, due to the limitations of the signal-to-noise ratio and sampling rate in the early studies, most fMRI studies focused on a composite band. And few studies explored the functional differences of BOLD oscillations at different frequencies. Although single-band fMRI research has made great progress in the localization of brain function and recognition of brain spontaneous networks, the frequency information contained in the brain oscillations has been ignored in most traditional fMRI studies. In the last decade, more and more scientists began to pay attention to the activation patterns of BOLD oscillations in different frequency bands. This is a step closer to unraveiling the mystery of brain function by using fMR. In order to fully understand the progress of multi-band fMRI research, we conducted a bibliometric analysis of an early study on multi-band fMRI analysis using theory-based frequency decomosition method, and systematically reviewed the results of multiband fMRI research in the past decade. There are three limitations in the existing multi-band fMRI research: First, different research adopted different frequency decomposition methods, which includes experience-based method, theory-based method and data-driven method. It is difficult to directly compare the results of studies using different methods. Second, the existing multi-band studies are mainly resting-state studies and clinical studies, so there is a lack of direct evidence of the functions of different frequency bands. Third, most resting-state studies focused on the comparison between slow-4 and slow-5 bands, thus lacking systematic research on the organization of spontaneous activity across the whole frequency bands. Based on the existing results, we summarized the spontaneous activity patterns of BOLD oscillations in different frequency bands, hypothesized the functions of different frequency bands, and proposed a theoretical model to describe the function and interaction of BOLD oscillations in different frequency bands – the three-level interaction model. The main scientific question of this study is to investigate the function of BOLD oscillations in different frequency bands, and preliminarily verify the theoretical model. Based on the limitations of existing studies, the following three aspects were carried out: (1) We determined the frequency decomposition method applicable to BOLD oscillations, and clarified its calculation method in practical application; (2) Multi-band analysis of task-state fMRI research was carried out to directly locate different functional activated bands; (3) The functional connectome gradient analysis method was used to study the spontaneous activity organization of the large-scale network of BOLD oscillations in different frequency bands and the development pattern at school age. The main research questions and results include: Study 1: Calculation and application of the natural logarithm linear law (N3L). Firstly, we determined the calculation of utilizing the N3L on frequency decompositino for discrete data. And we developed a toolbox for frequency decomposition based on the N3L. Then, taking multi-band analysis of head motion signals as an example, we explored the general application of the N3L-based method in physiological oscillations. Based on the head motion data of 84 healthy children aged 3 to 16 years, we revealed the developmental effects and gender differences in head motion oscillations at different frequency bands. Study 2: The function of multi-band BOLD oscillations. In this study, task-state fMRI data with high sampling rate from the Human Connectome Project (HCP) S1200 database were used to study the activation patterns of BOLD oscillations in different frequency bands under VI different tasks. The results are cosistent with the assumptions in the three-level interaction model. That is, oscillations of slow-1 to slow-3 bands are mainly involved in the detection and preliminary processing of visual and auditory stimuli. Slow-4 band is involved in motor function and various cognitive functions. Slow-5 band plays an important role in complex cognitive functions and memory. Study 3: Functional connectome gradient analysis of multi-band BOLD oscillations. Based on the resting-state fMRI data with high sampling rate from HCP S1200 dataset, we performed gradient analysis to study the spontaneous organization of the large-scale network in different frequency bands. The results show that, the first and second gradients have similar distribution patterns across frequency bands as in the traditional resting-state interval. The first gradient shows gradient changes from transmodal brain region to unimodal brain region, and the second gradient shows gradient changes between different modules of the primary sensory cortex. However, the gradient distribution of slow-1 and slow-2 bands differs in details with lower bands, which may reflect the difference between functional segregation and functional integration. Based on the development data collected by Chinese Color Nest Project (CCNP) in Chongqing, we investigated the development pattern of gradients in slow-3 to slow-5 bands. The results show that: (1) The developmental process shows three distinct patterns, which reflects that there are stages in the development of brain function and possible critical stages of development; (2) The development patterns of different frequency bands differentiate greatly, which is also a reflection of the functional differences of different frequency bands. This study explored the function of multi-frequency BOLD oscillations from theory to demonstration. The functional organization pattern of BOLD oscillations is proposed theoretically, and the frequency decomposition method suitable for BOLD oscillations is determined. Multi-band brain activation analysis of task-state fMRI data with high sampling rates provided direct evidence of different band functions. We studied the organization patterns of spontaneous brain networks in different frequency bands and revealed the development patterns of functional connectome gradients in different frequency bands. |
| 英文摘要 | 大脑是神经系统中重要而复杂的结构。解码其运作原理，是神经科学的终极目标。大脑中的神经元通过产生动作电位进行信息的传递与处理。这些微观的电信号可以通过不同的技术直接或间接地探测，形成不同水平的神经波动信号。一个世纪以来，应用脑电图等技术对人脑功能的研究发现，不同的脑功能及生理或心理状态会产生不同频段的神经波动。而功能磁共振研究由于早期受限于技术的信噪比、采样率等原因，主要聚焦在一个复合波段进行，很少研究不同频率血氧水平依赖波动的功能异同。尽管单一频段的功能磁共振研究在初步定位不同脑区的功能、识别大脑自发神经活动网络方面有了很大的推进，但是大脑神经波动中包涵的频率信息却在这些研究中忽视了。近十年来，越来越多的科学家开始关注不同频段血氧水平依赖波动的活动模式差异，以及不同频段波动与临床症状的关联。这对于应用功能磁共振技术来解析大脑的运作原理更近了一步。 为了全面了解多频段功能磁共振研究的进展，我们对基于理论的频段划分方法进行功能磁共振多频段分析的研究进行了文献计量学分析，系统综述了十年来多频段功能磁共振研究的现状及研究结果。结果发现，现有的多频段功能磁共振研究中存在三个主要的局限：第一，对于血氧水平依赖波动的频段划分方式并不统一。采用不同划分方法的研究结果很难直接进行比较。第二，现有研究主要以静息态研究、临床研究为主，缺少不同频段血氧水平依赖波动功能的直接证据。第三，大多数静息态研究聚焦 slow-4 和slow-5 两个频段间活动模式差异的对比，缺少对全频段自发活动组织形式的系统研究。根据现有研究结果，我们归纳总结了不同频段血氧水平依赖波动的自发活动模式，并对不同频段波动的功能进行了假设，提出了一个描述不同频段血氧水平依赖波动功能及相互作用关系的理论模型——“三层级交互模型”。本研究的主要研究问题为：探索不同频段血氧水平依赖波动的功能，并对我们提出的理论模型进行初步验证。具体地，结合现有研究中存在的局限性，从以下三个方面进行：(1)确定适用于血氧水平依赖波动的频段分解方法，并明确其在实际应用中的计算方式；(2)通过对任务态功能磁共振研究进行多频段分析，对不同功能激活频段进行直接定位，对“三层级交互模型”涉及的频段功能进行初步验证；(3)采用功能连接梯度分析方法研究不同频段血氧水平依赖波动宏观网络的自发活动组织模式，及其在学龄期的发育模式，对“三层级交互模型”涉及的频域组织层级进行初步验证。主要研究内容及研究结果包括： 研究一，自然对数线性理论的多频段计算与应用。自然对数线性理论是目前唯一的适用于神经波动分类与频段划分的理论，但其在离散信号中的计算方式并未明确。因此，这部分研究的主要目的是对应用自然对数线性理论进行离散数据频段划分的计算方法进行确定。我们根据尼奎斯特采样定理，明确了离散数据最高、最低频率界限的计算方法，以及不同频段上下界限的计算方法。并开发了一款基于自然对数线性理论的频段分解计算工具包。然后，以扫描仪内头动时间序列的多频段分析为例，对自然对数线性理论在生理波动的普适性应用进行了探索。基于 84 名 3 至 16 岁健康儿童被试的扫描仪内头动数据，我们发现不同频段头动波动存在发育效应及性别差异。首先，不同频段头动 FD 值均随龄减小；其次，在 7 至 9 岁年龄段，存在频率与性别交互作用，女孩在低频段头动显著低于男孩。 研究二，多频段血氧水平依赖波动的功能研究。这部分的研究目的为，通过分析大脑在进行不同任务时不同频段的激活模式，为不同频段血氧水平依赖波动的功能提供直接证据。我们采用人脑连接组计划 S1200 数据库中高采样率的任务态功能磁共振数据，对 7 种不同的任务范式进行多频段脑激活分析。研究结果基本验证了“三层级交互模型”中对不同频段波动的功能假设，研究结果显示：slow-1 至 slow-3 频段波动主要参与视觉、听觉刺激的探测和初步加工，且视听觉刺激的初步多脑区信息整合在 slow-3 频段进行。slow-4 频段波动参与躯体运动功能以及各种认知功能(如，工作记忆、算术、心理理论等)，且不同任务的激活模式呈现出模态分化。slow-5 频段波动在复杂认知功能及记忆功能中起重要作用，相较于 slow-4 频段，slow-5 频段的任务激活脑区更加泛化。 、研究三，多频段血氧水平依赖波动的功能连接梯度研究。本研究的研究目的为：探索不同频段血氧水平依赖波动的自发活动组织模式，及其发育规律。具体分为两部分进行研究。第一部分为多频段梯度的分布模式研究。这部分基于人脑连接组计划 S1200 数据库中高采样率的静息态功能磁共振数据，采用功能连接梯度分析方法研究全频段血氧水平依赖波动的自发活动组织模式。研究结果发现，在年轻成年人水平，第一梯度和第二梯度在整个慢波频段表现出与传统静息态频段相似的梯度模式——第一梯度主要呈现出 transmodal 脑区至 unimodal 脑区的梯度变化，第二梯度呈现出初级感觉皮层不同模块间的梯度变化。而 slow-1 和 slow-2 频段与较低频段间则表现出一定的差异性。第二部分为多频段梯度的发育模式研究。基于“中国彩巢计划”重庆地区采集的发育数据，我们分析了学龄期 slow-3 至 slow-5 频段梯度分布的发育模式。研究结果发现：(1)梯度分布在学龄期的发育过程呈现出逐渐变化的三个阶段，这体现了脑功能发育的阶段性，以及可能存在的发育关键期；(2)不同频段的发育模式有较大的差异性，这也是不同频段的功能差异的一种体现。 本研究对血氧水平依赖波动的功能进行了从理论到实证的探索。从理论层面上提出了血氧水平依赖波动的功能组织模型，确定了适用于血氧水平依赖波动的频率分解方法，并通过对高采样率的任务态功能磁共振数据进行多频段脑激活分析，提供了不同频段功能的直接证据；采用高采样率的静息态功能磁共振数据对不同频段自发脑网络组织模式进行了研究，揭示了不同频段功能连接梯度的发育模式。 |
| 语种 | 中文 |
| 源URL | [http://ir.psych.ac.cn/handle/311026/43151]  |
| 专题 | 心理研究所_健康与遗传心理学研究室 |
| 推荐引用方式 GB/T 7714 | 宫竹青. 人脑血氧水平依赖波动的多频段功能研究[D]. 中国科学院心理研究所. 中国科学院大学. 2022. |
入库方式： OAI收割
来源：心理研究所
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