Examining the electroencephalographic fingerprint of default mode network hyperconnectivity for scalable and personalized neurofeedback in schizophrenia

检查默认模式网络超连接的脑电图指纹，以实现精神分裂症的可扩展和个性化神经反馈

• 批准号: 10509002
• 负责人: Clemens Christian Chimalpopoca Bauer Hoss
• 金额: $ 20.1万
• 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: 2022-08-02 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10509002
• 关键词: Address; Adult; Auditory Hallucination; Auditory area; Brain; Brain region; Categories; Cellular Phone; Communication; Complementary therapies; Complex; Computer Analysis; Diagnosis; Diagnostic; Distress; Electrodes; Electroencephalography; Electrophysiology (science); Fingerprint; Frequencies; Functional Magnetic Resonance Imaging; Functional disorder; Future; Gold; Imaging technology; Individual; Intervention; Lead; Learning; Location; Machine Learning; Magnetic Resonance Imaging; Maps; Measurement; Measures; Mental disorders; Modality; Modeling; Network-based; Neuroanatomy; Neurosciences; Participant; Patients; Pattern; Performance; Persons; Pharmaceutical Preparations; Pharmacology; Pharmacotherapy; Procedures; Psychopathology; Relapse; Research; Research Domain Criteria; Research Personnel; Resistance; Rest; Sampling; Scalp structure; Schizophrenia; Severities; Signal Transduction; Source; Superior temporal gyrus; Symptoms; System; Techniques; Technology; Testing; Time; Training; Translating; Validation; Visual; Volition; base; clinical application; cognitive control; computational neuroscience; data acquisition; design; disability; effective therapy; experience; functional magnetic resonance imaging/electroencephalography; hemodynamics; imaging study; improved outcome; innovation; mindfulness; neurofeedback; neuropsychiatry; novel; novel therapeutics; personalized medicine; portability; predictive modeling; side effect; standard care; supervised learning; treatment strategy

PROJECT ABSTRACT Auditory hallucinations (AHs) are one of the core symptoms of schizophrenia (SZ) and constitute a significant source of suffering and disability. One third of SZ patients experience pharmacology-resistant AHs, such that it is imperative to develop alternative/complementary treatment strategies. Researchers are beginning to appreciate how mental illnesses are associated with specific changes in the complex patterns of communication between different brain regions thanks to new advances in Magnetic Resonance Imaging (MRI). In particular, innovations in functional Magnetic Resonance Imaging (fMRI) data acquisition and computational analysis, make it now possible to reliably map the functional neuroanatomy of brain networks in a personalized way, offering a potential avenue for identifying unique and individualized neurotherapeutic targets. Moreover, it is now possible to tailor a personal and noninvasive intervention to help patients normalize communication within and between complex brain networks using real-time neurofeedback— whereby patients observe and learn to regulate selected aspects of their own brain activity—. AHs are characterized by elevated intrinsic functional connectivity within the default mode network (DMN) and between DMN and other large-scale networks like the frontoparietal control network (FPCN) and auditory cortices (i.e., superior temporal gyrus (STG)). We recently developed an innovative real-time fMRI circuit neurofeedback (rt-fMRI-NF) paradigm whereby people observe a visual display of ongoing DMN activation levels and use mindfulness as a strategy to volitionally regulate this difference. Our research has shown that rt-fMRI-NF reduces DMN hyperconnectivity and increases DMN-FPCN anticorrelations, with a correlated reduction of AHs among adults diagnosed with SZ. Unfortunately, to target the major brain networks that function abnormally in neuropsychiatric conditions, neurofeedback currently relies on fMRI technology, which is an expensive procedure involving a complex setup and patient burden. Since frequency- specific components of electroencephalography (EEG) signals recorded on the scalp can serve as correlates of fMRI activity patterns, including DMN activity and connectivity. Here we propose to validate the EEG correlates of DMN interactions implicated in AHs using concurrent EEG-fMRI and to develop an EEG “fingerprint” of these fMRI network dynamics. Hence, we will expand our successful rt-fMRI-NF strategy with the innovative addition of concurrent EEG measurements. We will apply the latest advances in personalized fMRI functional network mapping to define the features of EEG signal to predict and optimize the EEG fingerprint of fMRI activity using advances in machine learning for bio-signals that may lead to future personalized, network- based EEG neurofeedback circuit therapy for AHs in SZ. This study will offer key technical innovations that could lead to novel and scalable clinical applications. We will richly (>30 minutes) sample 40 patients with SZ and AHs with simultaneous EEG-fMRI to develop a pioneering and personalized EEG fingerprint of DMN dynamics and so enable a scalable form of accurate network-based neurofeedback training to patients.

项目摘要 听觉幻觉（AHS）是精神分裂症（SZ）的核心症状之一，构成了重要的 痛苦和残疾的来源。 SZ患者中有三分之一经历了耐药性AHS，因此 必须制定替代/补充治疗策略。研究人员开始 欣赏精神疾病如何与复杂沟通模式的特定变化相关联 在不同的大脑区域之间，要归功于磁共振成像（MRI）的新进展。尤其， 功能磁共振成像（fMRI）数据采集和计算分析的创新，使 现在可以以个性化的方式可靠地绘制脑网络的功能性神经解剖学，提供 识别独特和个性化神经治疗靶标的潜在途径。而且，现在有可能 量身定制个人和非侵入性干预措施，以帮助患者正常于内部和之间的沟通 使用实时神经反馈的复杂大脑网络 - 患者观察并学会调节 他们自己的大脑活动的选定方面。 AHS的特征是固有功能连接升高 在默认模式网络（DMN）以及DMN和其他大规模网络之间 控制网络（FPCN）和听觉皮层（即，上级临时回（STG））。我们最近开发了 创新的实时fMRI电路神经反馈（RT-FMRI-NF）范式，人们可以观察到视觉显示 正在进行的DMN激活水平，并将正念用作自愿调节这种差异的策略。我们的 研究表明，RT-FMRI-NF降低了DMN的超连接性并增加了DMN-FPCN的反相关性， 在被诊断为SZ的成年人中，AHS的减少相关。不幸的是，针对主要大脑 神经反馈目前依赖于fMRI，在神经精神疾病状态下完全发挥作用的网络 技术，这是一个昂贵的程序，涉及复杂的设置和患者伯恩。由于频率 - 记录在头皮上的脑电图（EEG）信号的特定组件可以用作相关性 fMRI活性模式，包括DMN活性和连通性。在这里，我们建议验证脑电图 使用并发的EEG-FMRI在AHS中实现的DMN交互的相关性并开发EEG 这些fMRI网络动力学的“指纹”。因此，我们将扩大成功的RT-FMRI-NF策略 并发脑电图测量的创新添加。我们将应用个性化的最新进展 fMRI功能网络映射以定义脑电图信号的特征以预测和优化脑电图指纹 使用机器学习的进步来实现生物信号的fMRI活动，这可能会导致未来的个性化，网络 - 基于SZ中AHS的EEG神经反馈电路疗法。这项研究将提供关键的技术创新 导致新颖且可扩展的临床应用。我们将丰富（> 30分钟）样品40例SZ和AHS患者 使用简单的EEG-FMRI开发DMN动力学的开创性和个性化的脑电图指纹 因此，向患者启用一种可扩展的基于网络的神经反馈训练的形式。