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

脑电神经反馈是一种计算机引导的训练方法，它使训练能够对自己的大脑信号进行自愿控制。由于这些在神经精神障碍中通常是异常的，EEG NF为神经精神疾病提供了一个有前途的替代或补充治疗方案，包括作为一种常见疾病的严重抑郁障碍(MDD)，影响了近20%的人口(Wittchen等人2010)。然而，EEG NF的疗效仍然很低。一直以来，有相当一部分人(30%至40%)学习神经营养知识无效，因此是神经营养知识文盲(Arns等人，2014年；Birbaumer等人，2009年)。在MDD中，这也是正确的。这种文盲并不是MDD所特有的--这是一个长期存在的问题，在整个脑电神经反馈领域一直很普遍。除了一项动物研究表明皮质纹状体的可塑性很重要外，脑电-神经营养不良的潜在神经生理学机制，特别是在人类中，还没有被阐明。由于我们最近在同步EEG/fMRI的方法学上的发展，以及之前对MDD动态网络障碍的研究，我们现在处于一个独特的位置来研究成功和不足的脑神经营养因子学习的潜在神经生理脑机制，并利用这一理解来提高个体神经营养因子的成功率。在拟议的项目中，我们的目标是：1)通过同步的EEG/fMRI设置获得对EEG NF学习和相关文盲的潜在神经生理机制的见解，从而允许在fMRI扫描期间进行EEG NF训练。2)传递检测到的机制的知识以实现我们的第二个目标：通过使用在EEG NF训练期间实时从MRI扫描仪获得的脑状态功能信息来增强EEG NF学习成绩。我们将建立和验证一种新的方法：同时EEG/fMRI复合神经反馈。我们将通过1)多模式神经反馈的实时整合和2)对新获得的和先前存在的数据集的动态分析来实现这一目标。对生成的数据进行二次开发的目的是定义3)文盲的个体功能磁共振预测指标，4)成功的神经反馈过程中的功能网络定义，以及5)与抑郁症功能靶点的转移效应相关的大脑网络。

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.