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: 10675554
• 负责人: Clemens Christian Chimalpopoca Bauer Hoss
• 金额: $ 23.93万
• 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/10675554
• 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; Individual; Intervention; 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; clinical application; cognitive control; computational neuroscience; data acquisition; design; disability; effective therapy; experience; hemodynamics; improved; improved outcome; innovation; mindfulness; neurofeedback; neuropsychiatry; novel; novel therapeutics; personalized medicine; pharmacologic; portability; predictive modeling; side effect; standard care; supervised learning; therapy development; 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 幻听（AH）是精神分裂症（SZ）的核心症状之一，也是一种重要的症状。 三分之一的 SZ 患者经历了药物耐药性 AH，因此 研究人员开始开发替代/补充治疗策略。 了解精神疾病如何与复杂的沟通模式的特定变化相关 特别是，由于磁共振成像（MRI）的新进展，不同大脑区域之间的差异。 功能性磁共振成像 (fMRI) 数据采集和计算分析方面的创新，使得 现在可以以个性化的方式可靠地绘制大脑网络的功能神经解剖图，从而提供 确定独特和个性化神经治疗靶点的潜在途径此外，现在已经成为可能。 量身定制个人和非侵入性干预措施，帮助患者内部和之间的沟通正常化 使用实时神经反馈的复杂大脑网络——患者观察并学习调节 他们自己的大脑活动的某些方面的特点是内在功能连接性的提高。 在默认模式网络 (DMN) 内以及 DMN 和其他大型网络（如额顶网络）之间 我们最近开发了一个控制网络（FPCN）和听觉皮层（即颞上回（STG））。 创新的实时功能磁共振成像电路神经反馈（rt-fMRI-NF）范例，人们可以通过它观察视觉显示 持续的 DMN 激活水平，并使用正念作为自愿调节这种差异的策略。 研究表明 rt-fMRI-NF 降低了 DMN 超连接性并增加了 DMN-FPCN 反相关性， 不幸的是，被诊断患有精神分裂症的成年人的 AH 会相应减少，以主要大脑为目标。 在神经精神疾病中功能异常的网络，神经反馈目前依赖于功能磁共振成像 技术，这是一个昂贵的过程，涉及复杂的设置和患者负担。 头皮上记录的脑电图 (EEG) 信号的特定成分可以作为相关因素 fMRI 活动模式，包括 DMN 活动和连接性在这里我们建议验证 EEG。 使用并发 EEG-fMRI 关联 AH 中涉及的 DMN 相互作用并开发 EEG 因此，我们将通过这些 fMRI 网络动态的“指纹”来扩展我们成功的 rt-fMRI-NF 策略。 我们将应用个性化脑电图测量的最新进展。 fMRI功能网络映射定义脑电图信号的特征以预测和优化脑电图指纹 利用生物信号机器学习的进步进行功能磁共振成像活动，这可能会导致未来的个性化、网络化 基于脑电图神经反馈回路疗法对深圳的 AH 进行这项研究将提供关键的技术创新。 我们将对 40 名 SZ 和 AH 患者进行丰富（>30 分钟）采样。 与同步 EEG-fMRI 一起开发 DMN 动力学的开创性和个性化 EEG 指纹 因此，可以为患者提供一种可扩展的基于网络的精确神经反馈训练。