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

项目摘要 幻听(AHS)是精神分裂症(SZ)的核心症状之一 痛苦和残疾的根源。三分之一的SZ患者经历了药理耐药的AHS，因此 必须制定替代/补充治疗战略。研究人员开始研究 了解精神疾病如何与复杂沟通模式中的具体变化联系在一起 这要归功于磁共振成像(MRI)的新进展。特别是， 在功能磁共振成像(FMRI)数据采集和计算分析方面的创新，使 现在有可能以个性化的方式可靠地绘制大脑网络的功能神经解剖图，提供 确定独特的和个性化的神经治疗靶点的潜在途径。此外，现在有可能 量身定做个性化的非侵入性干预措施，帮助患者在内部和之间实现正常沟通 使用实时神经反馈的复杂大脑网络--患者通过观察并学习调节 他们自己大脑活动的精选方面-。AHS的特征是内在功能连接性升高 在默认模式网络(DMN)内以及在DMN和其他大型网络(如前顶层)之间 控制网络(FPCN)和听觉皮质(即颞上回(STG))。我们最近开发了一种 创新的实时fMRI电路神经反馈(RT-fMRI-NF)范例，人们可以通过它观察视觉显示 并使用正念作为一种策略来随意调节这种差异。我们的 研究表明，RT-fMRI-NF降低了DMN的超连通性，并增加了DMN-FPCN的反相关性， 在被诊断为SZ的成年人中，AHS的相关性降低。不幸的是，要瞄准大脑皮层 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 技术，这是一个昂贵的程序，涉及复杂的设置和患者的负担。因为频率- 记录在头皮上的脑电(EEG)信号的特定成分可以作为 FMRI活动模式，包括DMN活动和连接性。在这里，我们建议验证脑电 同步EEG-fMRI与AHS患者DMN相互作用的相关性及脑电研究 这些fMRI网络动态的“指纹”。因此，我们将通过以下方式扩展我们成功的RT-fMRI-NF策略 创新地增加了同步脑电测量。我们将在个性化方面应用最新进展 利用fMRI功能网络映射确定脑电信号特征以预测和优化脑电指纹 使用机器学习中的先进生物信号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- 基于脑电神经反馈电路疗法治疗深圳地区AHS这项研究将提供关键的技术创新， 导致新的和可扩展的临床应用。我们将对40名SZ和AHS患者进行充分的(30分钟)抽样 与同步EEG-fMRI一起开发具有开创性和个性化的DMN动力学和 因此，为患者提供一种可扩展的基于网络的准确神经反馈培训。

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.