| 题名 | 自发性脑神经振荡表征的静息态信息流及其认知关联研究 |
| 作者 | 孟静 |
| 答辩日期 | 2021-01 |
| 文献子类 | 硕士 |
| 授予单位 | 中国科学院心理研究所 |
| 授予地点 | 中国科学院心理研究所 |
| 其他责任者 | 李锐 |
| 关键词 | 静息态功能磁共振 低频振幅,活动信息流 活动信息流 功能连接 韦氏成人智力测试 |
| 学位名称 | 理学硕士（同等学力硕士） |
| 其他题名 | Resting-State Activity Flow Characterized by the Amplitude of Low-Frequency Fluctuation and its Cognitive Relevance |
| 学位专业 | 发展与教育心理学 |
| 中文摘要 | The Amplitude of Low Frequency Fluctuation (ALFF) proposed by Zang YF et al in 2007 is a fundamental index charactering spontaneous fluctuations in the resting brain. The ALFF has been widely used in cognitive and brain disease researches. It is generally believed that the ALFF is a representative research based on theory of functional separation that mainly reflects local oscillation and function of brain regions. Recently, several studies have revealed that the task-evoked regional activations can be predicted by interareal activity flow (AF) across the brain. Moreover, task activation and ALFF may have a common "source", suggesting that the resting-state ALFF may not only represent the functional activities of local brain regions but also reflect the exchange of information between interconnected regions in the brain. Using exploratory analysis and validation analysis, this study demonstrated that resting-state ALFF flows through intrinsic functional connectivity (FC) pathways based on the fMRI data of Nathan S. Kline Institute for Psychiatric Research (NKI-Rockland Sample) and the data from 1000 Functional Connectomes (FC1000Beijing_Zang). We hypothesized that the ALFF in local brain region can be predicted by estimated AF over resting-state FC networks. The fMRI data was first used to calculate the ALFF for the 160 brain regions of interest (ROIs) from the Dosenbach-6 network and 400-1000 ROIs from the Yeo-7 network, and the FC between these regions using Pearson correlations and multiple regression methods. The AF in each ROI was calculated as the FC-weighted sums of the ALFF of all other regions. Finally, spatial correlation between the ALFF and AF in whole brain and network levels was performed. The results showed that, in the whole brain and also in each functional network, there was a significant correlation between the ALFF and multiple regression-based AF, and the ALFF of the local brain region can be predicted by the AF from other regions. It is suggested that the ALFF represents the information interaction between the brain regions: not only of the whole-brain network but also within the local functional networks. This result can be reproduced and verified on different brain network maps and different independent samples. We also found that the FC calculated by multiple regression could improve the predictive effect of AF. Furthermore, this paper also explored the correlation of resting-state AF and ALFF with intelligence quotient (IQ) measured by the Wechsler Abbreviated Scale of Intelligence (WASI). The results showed that the correlation patterns of AF and ALFF with IQ were similar. Specifically, these regions were mainly concentrated in the default-mode network (DMN) and the frontal-parietal network, especially in the DMN areas such as the posterior cingulate/ precuneus and the bilateral inferior parietal lobule, suggesting the importance of DMN in gathering information for the whole-brain network. At the same time, the results supported the core theory of DMN and the parieto-frontal integration theory. The brain regions with significantly negative correlations between AF, ALFF and IQ scores were mainly concentrated in the sensorimotor cortex areas such as the lateral temporal lobe and the occipital lobe. This result suggests that the resting-state DMN may inhibit the sensorimotor cortext, and form two competing functional systems with the sensorimotor networks. In summary, ALFF not only characterizes the activity of local brain regions but also reflects that local spontaneous neural activities spread across brain regions and brain networks in the form of activity flows. At the same time, AF should be used as an index to describe the information flow attribute of the brain regions. It provides a new perspective for us to explore the mechanism of information communication between brain regions, to study the mechanism of spontaneous brain activities, and to explore the cognitive process and the pathological mechanisms of brain diseases. |
| 英文摘要 | 自发性脑神经振荡低频振幅（ALFF）自2007年首次提出以来，已成为衡量静息态脑功能活动的一项基础指标，广泛应用于认知和脑疾病研究，一般认为ALFF是功能分离的代表性研究方法，主要反映脑区局部的神经振荡和功能情况。近年来的多项研究发现，任务诱发的区域激活可通过跨脑区的活动信息流（Activity flow, AF）预测，且任务激活和ALFF或许有共同的“源”，提示我们静息态脑区的ALFF除了表征局部脑区的功能活动外，还可能体现脑网络中脑区间的信息交换。 本文基于美国纽约内森克兰精神病学研究所罗克兰样本库（NKI-Rockland Sample）和千人脑连接计划项目中的臧玉峰团队脑成像数据（FC1000 Beijing_Zang），通过探索性和可重复性分析，揭示静息态下脑区的ALFF反映脑自发活动通过功能连接（Functional connectivity, FC）通路进行流动和传播。研究假设，传递到某局部脑区的AF强度可以预测该脑区的ALFF。本研究基于Dosenbach-6网络160个感兴趣区（ROIs）和Yeo-7网络400-1000个ROIs两个独立脑图谱计算脑区的ALFF，采用Pearson相关方法和多元回归改进计算脑区间FC；使用除该脑区外所有脑区的ALFF以及到该脑区FC的加权和来模拟ALFF经FC通路的传播，用于估计传递到该脑区AF；并在全脑层面和大尺度功能网络层面分析ALFF和AF之间的空间相关性。结果表明，静息态ALFF与回归方法改进后的AF在全脑以及各功能网络的空间分布显著相关，局部脑区的ALFF可以通过全脑或其所在功能网络汇聚而来的AF进行预测，提示静息态下ALFF表征了脑区间的信息交互：既体现了全脑网络间的信息交互、也体现了局部功能网络内的信息交互，这一结论在不同脑网络图谱以及不同独立样本上均可重复验证。本文还发现，使用多元回归方法改进FC可以显著提高AF对目标脑区ALFF的预测性，说明相比传统的Pearson相关方法，多元回归方法可显著提升FC估计的准确性。最后，本文还探索了静息态AF和认知功能的相关性，本文结合韦氏智力测试成绩研究了AF、ALFF在部分脑区或脑网络层面与认知功能的相关性。结果显示，AF、ALFF与智商成绩显著正相关的脑区分布模式较为相似，主要集中在默认网络（DMN）的主要脑区以及额顶网络，尤其是后扣带回与楔前叶、双侧顶下小叶等DMN区域，提示DMN在全脑网络中汇聚信息的重要性，该结果支持已有的DMN核心中枢理论和智力额顶整合理论。AF、ALFF与智商成绩显著负相关的脑区主要集中在外侧颞叶、枕叶等感觉运动皮层区域，提示静息态下DMN或抑制感觉运动皮层，与感觉运动网络形成相互竞争的功能系统。 综上，ALFF不仅表征局部脑区活动程度，还反映了局部区域自发神经活动通过活动流的形式跨脑区、脑网络传播，同时AF作为一种描述脑区间信息流动属性的指标，为我们探究大脑脑区间信息沟通机制、研究脑自发性活动的工作机理以及探究认知过程或者脑疾病病理机制提供了一个新视角。 |
| 语种 | 中文 |
| 内容类型 | 学位论文 |
| 源URL | [http://ir.psych.ac.cn/handle/311026/41666]  |
| 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 | 孟静. 自发性脑神经振荡表征的静息态信息流及其认知关联研究[D]. 中国科学院心理研究所. 中国科学院心理研究所. 2021. |
| 个性服务 |
| 查看访问统计 |
| 相关权益政策 |
| 暂无数据 |
| 收藏/分享 |
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
修改评论