Trimodal imaging of human brain networks using simultaneous PET/MR/EEG

在过去的二十年里，数据驱动的处理方法已经确定了人类大脑的空间和暂时不同的功能网络，即使在所谓的“休息状态”中没有特定任务时，这些功能网络也是“活跃的”。许多这些静息状态网络（RSN）在受试者之间非常相似，并且RSN的改变与包括癫痫和痴呆在内的脑部疾病有关。RSN甚至可以在非人类物种中被识别出来，从而实现翻译方法并回答大脑功能的基本问题。然而，这些RSN的生理基础只是部分了解。绝大多数RSN研究是基于使用血氧水平依赖（BOLD）对比的功能性MRI，即由血流动力学变化引起的最小MRI信号波动。因此，这种方法只能间接推断神经元的活动，并且具有有限的时间分辨率。因此，从其他模式中识别rsn被认为是重要的一步。脑电图和脑磁图探测神经元信号，但通常仅限于表面记录。然而，使用适当的处理方法，可以识别出与fMRI中识别的网络具有相似拓扑结构的RSN。[18F]氟脱氧葡萄糖（fluorodeoxyglucose， FDG）-PET成像可以评估体内区域脑葡萄糖代谢（rCGM），并在可接受的空间分辨率但有限的时间分辨率下作为神经元能量消耗的替代品，因此，半定量静态[18F]氟脱氧葡萄糖-PET通常用于脑成像。在本研究中，我们将获取20名健康对照在静息状态下的完全同步、动态[18F]FDG-PET/fMRI/EEG数据，此外，使用简单的运动任务。我们将利用数据驱动的方法从这三种模态中提取RSN，并具体分析RSN在模态和时间尺度之间的时空关系。在最近的一项啮齿动物研究中，我们已经可以证明[18F]FDG-PET和fMRI衍生的rsn并不相同，更好地理解这些过程的生理学是至关重要的。现在，我们将把这些方法和发现转化到人类大脑中，并通过并行脑电图获取更直接的神经元信号来增加另一个时间和生理维度。利用内部资金，我们已经建立了必要的加工流水线，解决了该项目的技术前提。除了对健康志愿者进行扫描外，我们还将获得一组接受相同成像模式的癫痫患者，以验证这种疾病中描述的RSN改变，并更好地定义[18F]FDG-PET中常见的与致痫灶相关的rCGM下降的基础。这一建议的结果将对整个大脑连接社区具有高度重要性，并将进一步提供对大脑功能和不同成像模式下大脑网络解释的基本见解。

Over the last two decades data-driven processing methods have identified spatially and temporarily distinct functional networks of the human brain that are ‘active’ even at the absence of a specific task in the so-called ‘resting-state’. Many of these resting-state networks (RSN) are remarkably similar between subjects and alterations of RSNs have been associated with brain diseases including epilepsy and dementia. RSN could even be identified in non-human species enabling translational approaches and answering fundamental questions of brain functions. However, the physiological basis of these RSN is only partially understood. The vast majority of RSN studies is based on functional MRI using the blood oxygen level dependent (BOLD) contrast, i.e. minimal MRI signal fluctuations induced by hemodynamic changes. Thus, this approach can only indirectly infer neuronal activity and has limited temporal resolution. Therefore, identification of RSNs from other modalities is considered to be an important step. EEG and MEG probe a neuronal signal, but are usually limited to surface recordings. Still, using adequate processing methods, RSN could be identified that have a similar topology as the networks identified in fMRI. [18F]fluorodeoxyglucose (FDG)-PET imaging can assess regional cerebral glucose metabolism (rCGM) in-vivo and serves as a surrogate for neuronal energy consumption at acceptable spatial but limited temporal resolution, therefore, semi-quantitative static [18F]FDG-PET is generally used for brain imaging. Within this proposal we will acquire fully-simultaneous, dynamic [18F]FDG-PET/fMRI/EEG data in 20 healthy controls in the resting-state and, in addition, using a simple motor-task. We will extract RSN using data-driven methods from all three modalities and specifically analyze the spatial and temporal relation of RSN between the modalities and timescales. In a recent study in rodents we could already show that [18F]FDG-PET and fMRI derived RSNs are not identical and that a better understanding of the physiology of these processes is paramount. We will now translate the methods and findings to the human brain and add another temporal and physiological dimension by acquiring a more direct neuronal signal with parallel EEG. Using intramural funding, we have already established the necessary processing pipeline and solved the technical prerequisite for this project. In addition to scans in healthy volunteers, we will acquire a group of epilepsy patients undergoing the same imaging paradigm to validate RSN alterations described in this disease condition and better define the basis of the commonly observed decrease of rCGM in [18F]FDG-PET related to the epileptogenic focus. The results of this proposal will be of high importance for the whole brain connectivity community and will provide further, fundamental insights into brain functions and the interpretation of brain networks in the distinct imaging modalities.