Investigating electroencephalographic predictors of default mode network anticorrelation for personalized neurofeedback

项目总结/摘要 神经精神疾病越来越多地被理解为内在的功能性相互作用的障碍 广泛分布的大脑网络内部和之间。鉴于功能性磁共振成像的最新进展， 共振成像（fMRI）数据采集和计算分析，现在可以可靠地映射 功能神经解剖学的大脑网络在个人，提供了一个潜在的途径， 个性化的神经治疗靶点。然而，目前的金标准治疗（例如药物治疗） 精神病学实践最初并不是针对特定的大脑网络交互作用而设计的，并且缺乏协议 利用这种个人层面的数据。实时神经反馈-患者通过观察和学习 调节自己大脑活动的选定方面-是一种候选方法， 大脑网络内部和之间不健康的交流正常化。然而，为了瞄准少校， 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， 这是一种昂贵的手术，涉及复杂的设置和患者负担。该项目的目标是开发一个 功能磁共振成像网络动力学的脑电图（EEG）“指纹”，使基于神经反馈系统 在脑电图（电极放置在头皮上）单独可以用来精确地靶向内部和之间的相互作用 大脑网络由于EEG设备可以是便携式的，并且在灵活的设置中提供相对简单的设置， 这项工作可以实现一种可扩展的基于网络的神经反馈训练形式， access.我们的目标1是确定一个最佳的，可推广的EEG特征模型，预测功能磁共振成像- 基于默认模式网络（DMN）的“对抗”在个人。我们关注DMN的拮抗作用 因为这是一个主要特征，与精神疾病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EEG-fMRI数据（每个样本>100分钟）。 参与者），包括三个条件：（1）静息状态，（2）连续任务执行，和（3）连续 来自DMN拮抗状态的基于fMRI的神经反馈。我们将应用基于机器学习的方法， 识别EEG信号分量与基于fMRI的DMN拮抗作用之间的最佳映射。我们还 将确定需要多少个体水平的EEG-fMRI采样才能成功预测DMN EEG的拮抗作用。我们的目的2是测试DMN拮抗作用的EEG标记物是否预测 个体认知任务表现的波动。因此，我们的研究结果可以验证 行为相关的EEG神经反馈系统，将目标DMN拮抗作用。如果成功，我们 这项工作可以导致开发一种可访问的计算精神病学工具，可以在临床上进行测试。 DMN拮抗作用（和相关认知功能）受到影响的疾病，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.