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; 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.

项目摘要/摘要 神经精神疾病越来越被理解为内在功能相互作用的疾病 在广泛，分布式，大脑网络之间。鉴于功能磁性的最新进展 共振成像（fMRI）数据采集和计算分析，现在可以可靠地图 个体内大脑网络的功能性神经解剖学，为识别潜在的途径 个性化神经治疗靶标。但是，当前的黄金标准处理（例如药物治疗） 精神病实践最初不是为针对特定的大脑网络互动而设计的，并且缺乏协议 那利用这种个人级别的数据。实时神经反馈 - 患者观察并学会了 规范自己大脑活动的选定方面 - 是个人量身定制的候选方法 大脑网络之间和之间不健康的沟通的归一化。但是，针对专业 在神经精神病疾病中起作用的大脑网络，神经反馈依赖于fMRI，这是 一个昂贵的程序涉及复杂的设置和患者伯恩。该项目的目的是开发 FMRI网络动力学的脑电图（EEG）“指纹”，以便基于神经反馈系统 在脑电图（仅放置在头皮上）上，可以用来精确靶向内部和之间的相互作用 大脑网络。因为EEG设备可以便携，并在灵活设置中提供相对简单的设置，所以 工作可以实现一种基于网络的神经反馈培训的可扩展形式，患者可以定期使用该培训 使用权。我们的目标1是确定一个可预测fmri-的脑电图特征的最佳，可推广的模型 基于个人内部的默认模式网络（DMN）“对抗”。我们专注于这种DMN对抗 因为这是与经诊断的精神疾病认知功能障碍相关的主要特征 等级。我们将在24名健康成年人中收集高质量，简单的EEG-FMRI数据（每次抽样> 100分钟 参加），包括三个条件：（1）休息状态，（2）连续任务绩效，（3）继续 来自DMN拮抗作用的基于fMRI的神经反馈。我们将将基于机器学习的方法应用于 确定EEG信号成分与基于fMRI的DMN拮抗作用之间的最佳映射。此外，我们 将确定需要多少个个人级EEG-FMRI抽样来成功预测DMN 脑电图的对抗。我们的目标2是测试DMN拮抗作用的EEG标记是否可以预测 个人内部的认知任务绩效波动。因此，我们的发现可以提供对 靶向DMN拮抗作用的脑电图神经反馈系统的行为相关性。如果成功，我们的 工作可以导致开发可访问的计算精神病学工具，该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，包括注意力 - 赤字/多动障碍，抑郁和精神分裂症。