Development of Single Trial EEG-fMRI: Investigations of Dynamic Brain Function at High Temporal and Spatial Resolution

单次试验 EEG-fMRI 的开发：高时空分辨率下的动态脑功能研究

• 批准号: EP/F023057/1
• 负责人: Andrew Bagshaw
• 金额: $ 57.71万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2008
• 资助国家: 英国
• 起止时间: 2008 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FF023057%2F1
• 关键词: Development; Single; Trial; EEG; fMRI

In the last decade functional MRI (fMRI) has lead to substantial progress in understanding the human brain. fMRI mainly measures changes in blood flow in active regions of the brain. For example, if a subject watches a flashing screen the areas that process visual information respond, leading to an increase in blood flow. FMRI is extremely powerful because it is completely non-invasive and allows the repeated study of participants. However, it has two main drawbacks: it is an indirect marker of brain function, reliant on blood flow rather than electrical activity, and it is slow, since the changes in blood flow take several seconds to occur. This is much slower than the dynamic processing of the brain itself, as can be seen by attaching electrodes to the scalp and measuring the electric fields produced by the firing of active cells, a technique called electroencephalography (EEG). EEG shows that that the brain changes state on a timescale of milliseconds. So although fMRI is very powerful for locating which brain regions are involved in a task, more detailed information about the order in which they respond cannot easily be revealed.One way around this problem is to combine fMRI with EEG (EEG-fMRI), recording from electrodes while in the scanner. It is only in the last few years that the equipment and methods of analysis have been developed to accomplish it safely and effectively. The benefit of EEG-fMRI, compared with either technique alone, is that accurate timing and spatial information are both available, potentially providing a much more complete view of brain function. EEG-fMRI is increasingly widely used, but many questions remain to be answered, particularly concerning the best way to combine the two data sets. Until recently, EEG and fMRI data were usually averaged separately and compared across experimental conditions. However, a new method takes advantage of the variability that is observed in the EEG signal from stimulus to stimulus and uses it directly to integrate EEG and fMRI. Initial studies have shown considerable advantages over the standard analysis, consistent with previous work in EEG using categorisation and grouping of responses which suggests that a considerable amount of physiologically useful information is lost by averaging. This approach can also help to characterise more fully the relationship between EEG and fMRI themselves, which has been addressed using electrodes placed within the brains of anaesthetised animals, but which requires further validation in awake humans. The research not only has project-specific scientific goals, but also general methodological goals, relevant to the broad neuroscience community. The aim is to examine the relationship between EEG and fMRI responses to individual sensory events, focusing on the development of improved methods to understand the causes of variability in the response to repetitive stimuli, and hence improving characterisation of the dynamic function of the human brain. The project will perform four separate experiments using visual, auditory, motor and pain stimuli in order to characterise differences due to sensory modality, capitalising on inherent differences in the temporal dynamics of responses to constrain modelling methods. New analysis methods will be applied that utilise more fully the information available in the EEG and allow examination of its correlates in fMRI. The development of new techniques to utilise small differences in the brain's response from stimulus to stimulus is crucial to pinpoint the when and where of brain function, and in the future will open up new avenues of research to study more complex cognitive functions, such as learning, and brain diseases. This project will lay the groundwork that is necessary to understand the link between the two most widely available non-invasive techniques for studying the human brain, as well as providing insights into the way in which basic sensory information is processed.

在过去的十年中，功能性磁共振成像（fMRI）在理解人类大脑方面取得了重大进展。功能磁共振成像主要测量大脑活动区域的血流变化。例如，如果受试者观看闪烁的屏幕，处理视觉信息的区域会做出反应，导致血流增加。功能磁共振成像是非常强大的，因为它是完全非侵入性的，并允许重复研究的参与者。然而，它有两个主要缺点：它是大脑功能的间接标志物，依赖于血流而不是电活动，并且它很慢，因为血流的变化需要几秒钟才能发生。这比大脑本身的动态处理要慢得多，这可以通过将电极连接到头皮并测量由活跃细胞放电产生的电场来观察，这种技术称为脑电图（EEG）。脑电图显示，大脑在毫秒的时间尺度上改变状态。因此，尽管功能性磁共振成像在定位大脑区域参与某项任务方面非常强大，但要揭示它们反应顺序的更详细信息并不容易，解决这个问题的一种方法是将功能性磁共振成像与脑电图结合起来（联合收割机），在扫描仪中通过电极记录。只是在过去的几年里，才开发出了安全有效地完成这一任务的设备和分析方法。与单独使用这两种技术相比，EEG-fMRI的好处在于可以获得准确的时间和空间信息，从而可能提供更完整的大脑功能视图。脑电功能磁共振成像的应用越来越广泛，但仍有许多问题有待回答，特别是关于联合收割机结合两种数据集的最佳方法。直到最近，EEG和fMRI数据通常是分开平均的，并在不同的实验条件下进行比较。然而，一种新的方法利用了从刺激到刺激的EEG信号中观察到的可变性，并直接将其用于整合EEG和fMRI。初步研究表明，相当大的优势，标准的分析，与以前的工作在EEG中使用的分类和分组的反应，这表明，相当数量的生理上有用的信息丢失的平均。这种方法也有助于更全面地研究EEG和fMRI之间的关系，这一点已经通过在麻醉动物的大脑中放置电极来解决，但需要在清醒的人类中进一步验证。该研究不仅有特定项目的科学目标，而且还有与广泛的神经科学界相关的一般方法学目标。目的是研究EEG和fMRI对个体感觉事件的反应之间的关系，重点是开发改进的方法来了解重复刺激反应的变化原因，从而改善人脑动态功能的表征。该项目将使用视觉，听觉，运动和疼痛刺激进行四个独立的实验，以消除由于感觉模态引起的差异，利用反应时间动态的固有差异来约束建模方法。将应用新的分析方法，更充分地利用EEG中可用的信息，并允许在fMRI中检查其相关性。开发新技术来利用大脑对刺激的反应的微小差异对于确定大脑功能的时间和地点至关重要，并且在未来将开辟新的研究途径来研究更复杂的认知功能，例如学习和大脑疾病。该项目将奠定必要的基础，以了解两种最广泛使用的非侵入性技术之间的联系，用于研究人类大脑，以及提供对基本感官信息处理方式的见解。

项目成果

Dynamic spatiotemporal variability of alpha-BOLD relationships during the resting-state and task-evoked responses

• DOI: 10.1016/j.neuroimage.2017.04.051
• 发表时间: 2017-07
• 期刊: NeuroImage
• 影响因子: 5.7
• 作者: Stephen D. Mayhew;A. Bagshaw

Multimodal functional network connectivity: an EEG-fMRI fusion in network space.

多模态功能网络连接：网络空间中的 EEG-fMRI 融合

• DOI: 10.1371/journal.pone.0024642
• 发表时间: 2011
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Lei X;Ostwald D;Hu J;Qiu C;Porcaro C;Bagshaw AP;Yao D
• 通讯作者: Yao D

Scanning Strategies for Simultaneous EEG-fMRI Recordings

同时 EEG-fMRI 记录的扫描策略

• 作者: Andrew Bagshaw (Author)

A perceptual decision making EEG/fMRI data set

感知决策 EEG/fMRI 数据集

• DOI: 10.1101/253047
• 发表时间: 2018
• 作者: Georgie Y