Improving EEG reading of brain states for clinical applications using a data-driven joint model of FMRI and EEG

使用数据驱动的 FMRI 和 EEG 联合模型改善临床应用中脑状态的 EEG 读取

• 批准号: EP/I01487X/1
• 负责人: Richard Wise
• 金额: $ 13.56万
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FI01487X%2F1
• 关键词: Improving; EEG; reading; brain; states

In recent years researchers have learned a great deal about the function of the human brain through neuroimaging techniques. Different techniques have their own strengths and weaknesses and each offers a window on brain function with a different perspective. Two of the most common methods for measuring the amount and location of brain activity associated with different sensations, thoughts and feelings are electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). EEG records from electrodes attached to the scalp the electrical signals from co-ordinated activity of large numbers of nerve cells. FMRI, however, records using an MRI scanner, the local changes in blood oxygenation associated with alterations in neural activity. EEG has the advantage of being able to detect rapid changes in neural activity (millisecond temporal resolution) but suffers from a poor ability to pinpoint the location of brain activity (spatial resolution). FMRI, however, has good spatial resolution (a few millimetres) but poor temporal resolution (a few seconds) because the signal relies on changes in blood flow: the plumbing of the brain. FMRI and EEG can therefore be regarded as complementary with EEG giving the 'when' and fMRI giving the 'where' of brain activity.However, while very useful in research, doctors and scientists want also to develop these neuroimaging techniques for practical uses which rely on reading the state of the brain or measuring the activity of the brain. These include, but are not limited to, brain-computer interfaces (BCI, disabled patients using brain signals to control a device), assessment of the effects of new medicines targeted at the brain and the diagnosis of epilepsy (the type and source of seizures from within the brain). EEG has been used, for many years in some cases, in these applications and has the considerable advantage of being portable and comparatively cheap and therefore appropriate for a routine lab or clinical setting. Why is the usefulness of EEG limited? As we have seen, its spatial resolution is comparatively poor but it can also be insensitive because of many signals from the brain being present and mixing together. FMRI is a more recent technique able to discriminate very well different patterns of brain activity but requires an MRI scanner: clearly not portable and comparatively expensive. In research labs such as ours at Cardiff University Brain Research Imaging Centre (CUBRIC), it has become possible to perform EEG and fMRI simultaneously. Our research proposal aims to improve the ability of EEG to discriminate different brain states or responses to specific types of stimulation, such as pain, drugs or for control of BCIs. To exploit the day-to-day practical advantages of EEG we wish to improve its stand alone capabilities. We will use fMRI in this project to help us do this. How can fMRI help us to improve EEG? We will use EEG and fMRI measurements acquired simultaneously on healthy volunteers. We will relate these two types of measurements together in what it known as a statistical model derived from the data. This procedure will discover associations or correlations between the EEG and fMRI data. Subtle features of the EEG signal, which are not normally easily identified but which are associated with the spatial location of the source of neural activity, will be highlighted by their association with the fMRI data, which is good at pinpointing locations in space. Having established and codified the relationship between the EEG and fMRI data in mathematical terms, EEG data alone will be used to simulate fMRI scans. These simulated fMRI scans will be used, applying what we know about the representation of brain activity by fMRI, to interpret the EEG signal effectively improving its spatial resolution. This will improve the ability of EEG on its own to tell the difference between brain states for the uses in BCI, development of medicines and clinical conditions.

近年来，研究人员通过神经成像技术对人类大脑的功能有了大量的了解。不同的技术有自己的长处和短处，每一种技术都从不同的角度为大脑功能提供了一个窗口。测量与不同感觉、思想和感觉相关的大脑活动的数量和位置的两种最常用的方法是脑电图（EEG）和功能性磁共振成像（fMRI）。脑电图通过附着在头皮上的电极记录大量神经细胞协调活动产生的电信号。然而，功能磁共振成像（FMRI）使用核磁共振扫描仪记录了与神经活动改变相关的局部血氧变化。脑电图的优点是能够检测神经活动的快速变化（毫秒时间分辨率），但在精确定位大脑活动的位置（空间分辨率）方面能力较差。然而，FMRI具有良好的空间分辨率（几毫米），但时间分辨率较差（几秒），因为信号依赖于血流的变化：大脑的管道。因此，功能磁共振成像和脑电图可以被看作是互补的，脑电图给出了大脑活动的“时间”，功能磁共振成像给出了大脑活动的“地点”。然而，尽管在研究中非常有用，医生和科学家们也希望开发这些依赖于读取大脑状态或测量大脑活动的实际应用的神经成像技术。这些包括但不限于脑机接口（BCI，残疾患者使用大脑信号控制设备），评估针对大脑的新药的效果以及癫痫的诊断（大脑内癫痫发作的类型和来源）。在某些情况下，脑电图已经在这些应用中使用了多年，并且具有便携和相对便宜的相当大的优势，因此适用于常规实验室或临床环境。为什么脑电图的用处有限？正如我们所看到的，它的空间分辨率相对较差，但由于来自大脑的许多信号存在并混合在一起，它也可能不敏感。功能磁共振成像是一种较新的技术，能够很好地区分不同的大脑活动模式，但需要核磁共振成像扫描仪：显然不方便携带，而且相对昂贵。在我们位于卡迪夫大学脑研究成像中心（CUBRIC）的研究实验室中，同时进行脑电图和功能磁共振成像已经成为可能。我们的研究计划旨在提高脑电图识别不同大脑状态或对特定类型刺激（如疼痛、药物或脑机接口控制）的反应的能力。为了发挥脑电图在日常应用中的优势，我们希望提高其独立能力。我们将在这个项目中使用功能磁共振成像来帮助我们做到这一点。fMRI如何帮助我们改善脑电图？我们将使用同时获得的健康志愿者的脑电图和功能磁共振成像测量结果。我们将把这两种类型的测量联系在一起，这就是从数据中得出的统计模型。这个过程将发现脑电图和功能磁共振成像数据之间的关联或相关性。脑电图信号的微妙特征通常不容易识别，但与神经活动源的空间位置有关，这些特征将通过与fMRI数据的关联而突出，fMRI数据擅长精确定位空间位置。在用数学术语建立和编纂了脑电图和功能磁共振成像数据之间的关系之后，脑电图数据将单独用于模拟功能磁共振成像扫描。这些模拟的功能磁共振成像扫描将被使用，应用我们所知道的功能磁共振成像对大脑活动的表征，来有效地解释脑电图信号，提高其空间分辨率。这将提高脑电图在脑机接口、药物开发和临床条件下区分大脑状态的能力。