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

为了增加我们对大脑是如何工作的，以及患有癫痫等神经系统疾病的人大脑是如何出错的理解，我们需要开发更好的系统来测量大脑活动。脑电(EEG)是临床和实验神经科学中的一种重要手段，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，而功能磁共振成像(FMRI)可以在几秒钟的时间尺度和几毫米或更高的空间分辨率上绘制整个大脑的血液动力学变化。因此，他们可以一起对更大范围的大脑活动进行测量，这些活动发生在更快的时间尺度或更小的空间尺度上。然而，在同步EEG-fMRI采集期间，这两种方法的信号会因运动相关的噪声而退化，从而显著限制了此类研究的灵敏度。该应用程序的目的是建立一个健壮的系统，可以同时测量大脑活动的EEG和fMRI。为了实现这一目标，我们将整合新的快速fMRI脉冲序列，因为在较短的时间间隔内获得的图像本质上运动敏感度较低(就像相机上较快的快门速度)。此外，如果我们在单位时间内获得更多图像，将有可能对运动进行更好的建模，因为我们可以更好地分离和建模不同频率范围内发生的不同信号源和噪声源。此外，我们将使用相机系统优化运动检测和预期运动校正(PMC)，该系统跟踪受试者的运动并更新图像采集过程，从而抑制患者的运动。当将这些改进用于fMRI数据采集时，我们将需要开发新的EEG伪影校正方法，用于同时进行扫描仪内EEG记录，目前这种方法依赖于伪影的重复性。运动和PMC都可能使伪影更加多变，因此需要开发新的校正方法。一旦我们开发了这个系统，我们将把它应用于来自大奥蒙德街医院的10名难治性癫痫患者，他们正在接受癫痫手术评估。这项评估旨在识别癫痫的大脑区域，而EEG-fMRI是帮助获得这一信息的工具。我们将比较我们目前的标准EEG-fMRI协议，它经常由于运动(特别是在幼儿中)而退化，与我们新的健壮的EEG-fMRI结合了PMC、FAST 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.