Investigating electroencephalographic predictors of default mode network anticorrelation for personalized neurofeedback

研究个性化神经反馈的默认模式网络反相关的脑电图预测因子

• 批准号: 10447471
• 负责人: Aaron Kucyi
• 金额: --
• 依托单位: NORTHEASTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2022-05-02
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10447471
• 关键词: Address; Adult; Affect; Attention; Attention deficit hyperactivity disorder; Behavioral; Brain; Brain region; Categories; Clinical; Communication; Complex; Computer Analysis; Data; Development; Devices; Diagnostic; Disease; Electrodes; Electroencephalography; Electrophysiology (science); Feedback; Fingerprint; Frequencies; Functional Magnetic Resonance Imaging; Functional disorder; Future; Goals; Gold; Impaired cognition; Individual; Intervention; Lead; Learning; Location; Magnetic Resonance Imaging; Maps; Measurement; Measures; Mental Depression; Mental disorders; Methods; Modeling; Monitor; Network-based; Neuroanatomy; Participant; Patient Education; Patients; Pattern; Performance; Persons; Pharmacotherapy; Procedures; Process; Protocols documentation; Psychiatry; Psychopathology; Research Domain Criteria; Research Personnel; Rest; Sampling; Scalp structure; Schizophrenia; Signal Transduction; System; Task Performances; Techniques; Testing; Time; Validation; Work; antagonist; base; brain behavior; cognitive function; cognitive task; cohort; computational neuroscience; data acquisition; design; effective therapy; flexibility; functional magnetic resonance imaging/electroencephalography; hemodynamics; imaging study; machine learning method; neurofeedback; neurophysiology; neuropsychiatry; novel; personalized predictions; portability; predictive modeling; relating to nervous system; standard care; supervised learning; therapeutically effective; tool

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.

项目概要/摘要 神经精神疾病越来越被理解为内在的、功能性相互作用的疾病 广泛分布的大脑网络内部和之间。鉴于功能磁学的最新进展 共振成像 (fMRI) 数据采集和计算分析，现在可以可靠地绘制 个体大脑网络的功能神经解剖学，为识别提供了潜在的途径 个性化的神经治疗目标。然而，目前的黄金标准治疗（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而设计的，并且缺乏协议 利用此类个人级别的数据。实时神经反馈——患者观察并学习 调节自己大脑活动的选定方面——是个人定制大脑活动的候选方法 大脑网络内部和之间的不健康沟通的正常化。不过，为了瞄准主要 对于在神经精神疾病中功能异常的大脑网络，神经反馈依赖于功能磁共振成像（fMRI）， 这是一项昂贵的手术，涉及复杂的设置和患者负担。该项目的目标是开发一个 脑电图（EEG）功能磁共振成像网络动力学的“指纹”，以便基于神经反馈系统 单独使用脑电图（放置在头皮上的电极）可以精确定位内部和之间的相互作用 大脑网络。由于脑电图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，我们的 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，患者可以定期进行这种训练 使用权。我们的目标 1 是确定一个最佳的、可推广的脑电图特征模型，该模型可预测 fMRI- 基于默认模式网络（DMN）的个体内部“对抗”。我们关注这种 DMN 对抗 因为它是跨诊断时与精神疾病认知功能障碍相关的一个主要特征 等级。我们将收集 24 名健康成年人的高质量同步 EEG-fMRI 数据（每个样本采样时间超过 100 分钟） 参与者），包括三个条件：（1）静息状态，（2）连续任务表现，（3）连续 来自 DMN 拮抗状态的基于功能磁共振成像的神经反馈。我们将应用基于机器学习的方法 确定 EEG 信号分量与基于 fMRI 的 DMN 拮抗之间的最佳映射。此外，我们 将确定需要多少个体级 EEG-fMRI 采样才能成功预测 DMN 脑电图的拮抗作用。我们的目标 2 是测试 DMN 拮抗的 EEG 标记是否可以预测 个体内的认知任务表现波动。因此，我们的研究结果可以验证 针对 DMN 拮抗作用的 EEG 神经反馈系统的行为相关性。如果成功的话，我们的 这项工作可以开发出一种易于使用的计算精神病学工具，可以在临床中进行测试 DMN 拮抗（和相关认知功能）受到影响的情况，包括注意力 缺陷/多动症、抑郁症和精神分裂症。