Stratification and Augmentation of EEG-Neurofeedback in MDD by Monitoring of Dynamic Brain States via Simultaneous Electroencephalography and Magnetic Resonance (EEG-fMRI)

EEG神经反馈（NF）是一种计算机引导的训练方法，它可以训练对自己大脑信号的自愿控制。由于这些在神经精神疾病中通常是异常的，因此EEG NF为神经精神疾病提供了一种有希望的替代或补充治疗选择，包括作为影响近20%人群的常见疾病的重度抑郁症（MDD）（Wittchen et al 2010）。一直有相当大比例（30至40%）的人，其中NF学习是无效的，因此是NF文盲（Arns等人，2014; Birbaumer等人，2009年）。在MDD中，这也是正确的。这种文盲不是MDD特有的-这是一个长期存在的问题，在整个EEG神经反馈领域普遍存在。除了一项动物研究表明皮质纹状体可塑性是重要的，EEG-NF文盲的潜在神经生理学机制，特别是在人类中，尚未阐明。由于我们最近在同步EEG/fMRI和先前的MDD动态网络干扰的研究方法的发展，我们现在处于一个独特的位置，可以研究成功和不充分的EEG NF学习的潜在神经生理学大脑机制，并利用这种理解，在所提出的项目中，我们的目标是：1）获得EEG NF学习和相关文盲的潜在神经生理机制的见解，同时EEG/fMRI设置，允许在fMRI扫描期间进行EEG NF训练。2）转移检测到的机制的知识，以实现我们的第二个目标：通过使用来自EEG NF训练期间真实的时间的MRI扫描仪的功能性大脑状态信息来增强EEG NF学习性能。我们将建立并验证一种新的方法：同时EEG/fMRI复合神经反馈，通过1）多模态神经反馈的真实的时间整合的方法学研究和2）新获得和已有数据集的动态分析来实现这一目标。二次开发所产生的数据的目的是定义3）个人的功能磁共振成像预测文盲，4）功能网络的定义，在成功的神经反馈和5）与抑郁症的功能目标的转移效应的大脑网络。

EEG Neurofeedback (NF) is a computer guided training method, which enables to train for a voluntary control over ones own brain signals. Because these are often abnormal in neuropsychiatric disorders, EEG NF poses a promising alternative or supplementary treatment option for neuropsychiatric disorders, including Major Depressive Disorder (MDD) as a common disorder affecting almost 20% of the population (Wittchen et al 2010).However, EEG NF efficacy is still low. There has always been a considerable proportion (30 to 40%) of people in which NF learning is ineffective that are therefore NF illiterate (Arns et al., 2014; Birbaumer et al., 2009). In MDD this also holds true. This illiteracy is not specific for MDD - it is a long existing issue that has been prevalent in the entire field of EEG Neurofeedback. Apart from one animal study showing that corticostriatal plasticity is important, the underlying neurophysiological mechanisms of EEG-NF illiteracy, especially in humans, have yet to be elucidated.Due to our recent methodological developments in simultaneous EEG/fMRI and previous investigatoins of dynamic network disturbances in MDD, we are now in a unique position to examine the underlying neurophysiological brain mechanisms of successful and insufficient EEG NF learning and exploit this understanding for improving individual NF success rates.In the proposed project, we aim to:1) Gain insights on the underlying neurophysiological mechanisms of EEG NF learning and related illiteracy with a simultaneous EEG/fMRI setup that allows for EEG NF training during fMRI scanning.2) Transfer knowledge of the detected mechanisms to accomplish our second aim: to augment EEG NF learning performance by using functional brain state information from the MRI scanner derived in real time during EEG NF training. We here will establish and validate a new method: simultaneous EEG/fMRI compound neurofeedback.We will accomplish this aim by 1) methodological work on the real time integration of multimodal neurofeedback and 2) dynamic analysis of newly acquired and preexisting datasets. Secondary exploitation of the generated data aims at definition of 3) individual fMRI predictors of illiteracy, 4) functional network definition during successful neurofeedback and 5) brain networks associated to transfer effects on functional targets for depression.