The fast without the spurious: developing a system for robust and rapid simultaneous EEG-fMRI measurements

为了加深我们对大脑如何工作以及患有癫痫等神经系统疾病的人大脑如何出错的理解，我们需要开发更好的系统来测量大脑活动。脑电图（EEG）是临床和实验神经科学中的一种重要方式，能够以亚毫秒的时间分辨率和厘米的空间分辨率测量发生的电变化，而功能性磁共振成像（fMRI）可以在秒的时间尺度和几毫米或更好的空间分辨率下绘制整个大脑的血流动力学变化。因此，他们可以一起测量更大范围的大脑活动，无论是在更快的时间尺度上还是在更小的空间尺度上。然而，在同时进行EEG-fMRI采集时，这两种方法的信号都受到与运动相关的噪声的影响，从而极大地限制了这类研究的灵敏度。这个应用程序的目的是建立一个强大的系统，可以执行这些同时脑电图和功能磁共振成像测量大脑活动。为了实现这一目标，我们将集成新的快速fMRI脉冲序列，因为在较短的时间间隔内获得的图像本质上对运动不太敏感（就像相机上更快的快门速度）。此外，如果我们在单位时间内获得更多的图像，更好的运动建模将是可能的，因为我们可以更好地分离和建模不同频率范围内出现的不同信号源和噪声源。此外，我们将优化运动检测和前瞻性运动校正（PMC）使用相机系统，跟踪受试者的运动和更新图像采集过程，使患者的运动被抑制。当将这些改进应用于fMRI数据采集时，我们需要开发新的脑电图伪影校正方法，用于同时在扫描仪内进行脑电图记录，而这些方法目前依赖于伪影在时间上的重复性。运动和PMC都可能使伪影更加可变，因此需要开发新的校正方法。一旦我们开发出这个系统，我们将把它应用于大奥蒙德街医院的10名难以治疗的癫痫患者，他们正在接受癫痫手术的评估。这项评估的目的是确定癫痫的大脑区域和EEG-fMRI是一个工具，以帮助获得这一信息。我们将比较我们目前的标准EEG-fMRI方案，该方案经常因运动而退化（特别是在幼儿中），与我们新的强大的EEG-fMRI结合PMC，快速fMRI和改进的EEG伪影校正。

To increase our understanding of how the brain works and how it goes wrong in people with neurological conditions such as epilepsy we need to develop better systems for making measurements of brain activity. Electroencephalography (EEG) is an important modality in clinical and experimental neuroscience capable of measuring electrical changes occurring with sub-millisecond temporal resolution and a spatial resolution of centimetres while functional Magnetic Resonance Imaging (fMRI) can map haemodynamic changes over the entire the brain at a time-scale of seconds and spatial resolution of a few millimetres or better. Therefore together they can perform measurements across a greater range of brain activity occurring either at faster temporal or smaller spatial scales. However, during simultaneous EEG-fMRI acquisitions these both methods signal are degraded by noise related to motion, thereby significantly limiting the sensitivity of this type of study so far.The purpose of this application is to build a robust system that can perform these simultaneous EEG and fMRI measurements of brain activity. To achieve this goal we will integrate new fast fMRI pulse sequences because images obtained in shorter time intervals are intrinsically less motion sensitive (like a faster shutter speed on a camera). Also better modelling of motion will be possible if we obtain more images per unit time because we can better separate and model the different sources of signal and noise that occur in different frequency ranges. In addition, we will optimise motion detection and prospective motion correction (PMC) using a camera system that tracks the subject's motion and updates the image acquisition process so that patient motion is supressed. When using these improvements to fMRI data acquisition we will need to develop novel EEG artefact correction methods for simultaneous in-scanner EEG recording which currently rely on the repetitive nature of the artefact in time. Both motion and PMC are likely to make the artefact more variable and so will require the development of novel correction methods. Once we have developed this system we will apply it to ten patients with hard to treat epilepsy from Great Ormond Street Hospital who are being assessed for epilepsy surgery. This assessment aims to identify the epileptic brain regions and EEG-fMRI is a tool to help obtain this information. We will compare our current standard EEG-fMRI protocol which often suffers from degradation due to motion (particularly in young children) to our new robust EEG-fMRI incorporating PMC, fast fMRI and improved EEG artefact correction.