Investigating electroencephalographic predictors of default mode network anticorrelation for personalized neurofeedback

项目摘要/摘要 神经精神疾病越来越多地被理解为内在的、功能性的相互作用的障碍 在分布广泛的大脑网络内部和之间。鉴于功能磁学的最新进展 磁共振成像(FMRI)数据采集和计算分析，现在可以可靠地绘制 个体内大脑网络的功能神经解剖学，为识别 个性化的神经治疗靶点。然而，当前的黄金标准治疗(如药物治疗) 精神病学实践最初并不是针对特定的大脑网络相互作用而设计的，并且缺乏协议 利用这些个人层面的数据。实时神经反馈--患者通过观察并学习 调节自己大脑活动的选定方面-是一种个人定制 大脑网络内部和大脑网络之间不健康交流的正常化。然而，要瞄准少校 在神经精神疾病中，大脑网络功能异常，神经反馈依赖于功能磁共振成像，这是 这是一个昂贵的手术，涉及复杂的设置和患者的负担。这个项目的目标是开发一种 脑电“指纹”的fMRI网络动态使神经反馈系统基于 在脑电(放置在头皮上的电极)上，单独使用可以精确地定位内部和之间的相互作用 大脑网络。由于EEG设备可以是便携的，并且在灵活的设置中提供相对简单的设置，因此我们的 工作可以实现一种基于网络的可扩展形式的神经反馈培训，患者可以定期进行 进入。我们的目标1是确定一个最优的、可推广的脑电特征模型，这些特征可以预测功能磁共振成像- 基于默认模式的网络(DMN)在个体内部具有“对抗性”。我们关注的是DMN的对抗 因为这是一个主要特征，与精神病患者的认知功能障碍有关 水平。我们将收集24名健康成年人的高质量同步脑电-功能磁共振数据(每100分钟采样一次 参与者)，包括三个条件：(1)休息状态，(2)连续任务表现，(3)连续 基于fMRI的DMN拮抗状态的神经反馈。我们将把基于机器学习的方法应用于 确定脑电信号成分和基于功能磁共振成像的DMN拮抗之间的最佳映射。此外，我们 将确定需要多少个体水平的EEG-fMRI采样才能成功预测DMN 脑电波的对抗性。我们的目标2是测试DMN拮抗的脑电标志物是否可以预测 认知任务绩效在个体内的波动。因此，我们的发现可以提供对 以DMN拮抗为靶点的脑电神经反馈系统的行为相关性。如果成功，我们的 这项工作可能导致开发一种可访问的、可在临床上测试的计算精神病学工具 DMN拮抗(和相关认知功能)受到影响的情况，包括注意力- 缺陷/多动障碍、抑郁症和精神分裂症。

PROJECT SUMMARY/ABSTRACT Neuropsychiatric conditions are increasingly being understood as disorders of intrinsic, functional interactions within and between widespread, distributed, brain networks. Given recent advances in functional Magnetic Resonance Imaging (fMRI) data acquisition and computational analysis, it is now possible to reliably map the functional neuroanatomy of brain networks within individuals, offering a potential avenue for identifying personalized neurotherapeutic targets. However, gold standard treatments (e.g. pharmacotherapy) in current psychiatric practice were not originally designed to target specific brain network interactions and lack protocols that leverage such individual-level data. Real-time neurofeedback— whereby patients observe and learn to regulate selected aspects of their own brain activity— is a candidate approach to personally tailor the normalization of unhealthy communication within and between brain networks. However, to target the major brain networks that function abnormally in neuropsychiatric conditions, neurofeedback relies on fMRI, which is an expensive procedure involving a complex setup and patient burden. The goal of this project is to develop an electroencephalography (EEG) “fingerprint” of fMRI network dynamics so that a neurofeedback system based on EEG (electrodes placed on the scalp) alone can be used to precisely target interactions within and between brain networks. Because EEG devices can be portable and offer relatively simple setup in flexible settings, our work could enable a scalable form of network-based neurofeedback training that patients could regularly access. Our Aim 1 is to identify an optimal, generalizable model of EEG features that are predictive of fMRI- based default mode network (DMN) “antagonism” within individuals. We focus on this DMN antagonism because it is a major feature that is relevant to cognitive dysfunction in psychiatric disease at a transdiagnostic level. We will collect high-quality, simultaneous EEG-fMRI data in 24 healthy adults (>100 mins of sampling per participant), including three conditions: (1) resting state, (2) continuous task performance, and (3) continuous fMRI-based neurofeedback from DMN antagonism states. We will apply machine learning-based methods to identify an optimal mapping between EEG signal components and fMRI-based DMN antagonism. Further, we will determine how much individual-level EEG-fMRI sampling is needed to successfully predict DMN antagonism from EEG. Our Aim 2 is to test whether EEG markers of DMN antagonism are predictive of cognitive task performance fluctuations within individuals. As such, our findings could offer validation of the behavioral relevance of an EEG neurofeedback system that would target DMN antagonism. If successful, our work can lead to development of an accessible, computational psychiatry tool that can be tested in clinical conditions in which DMN antagonism (and related cognitive function) is affected, including attention- deficit/hyperactivity disorder, depression and schizophrenia.