Targeting large-scale networks in depression with real-time fMRI neurofeedback

通过实时功能磁共振成像神经反馈针对抑郁症的大规模网络

• 批准号: 10721968
• 负责人: Stephan F Taylor
• 金额: $ 42.9万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-06 至 2025-09-05
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10721968
• 关键词: Address; Affect; Anterior; Anxiety Disorders; Attention; Autobiography; Back; Behavioral Paradigm; Brain; Cognitive Therapy; Controlled Study; Development; Diagnostic; Double-Blind Method; Educational process of instructing; Electroencephalography; Emotions; Equilibrium; Exhibits; Feedback; Functional Magnetic Resonance Imaging; Individual; Insula of Reil; Intervention; Link; Magnetic Resonance Imaging; Major Depressive Disorder; Maps; Mediating; Memory; Mental Depression; Mental disorders; Methods; Modality; Neurosciences; Participant; Pathologic; Patient Education; Patients; Pattern; Persons; Protocols documentation; Randomized; Recurrence; Rest; Running; Sampling; Signal Transduction; Stimulus; Symptoms; Techniques; Testing; Therapeutic; Time; Training; Transcranial magnetic stimulation; Work; depressive symptoms; directed attention; emotion regulation; executive function; imaging study; improved; memory recall; mindfulness; neurofeedback; neuroimaging; personalized intervention; programs; psychologic; psychosocial; reduce symptoms; response; rumination; skills; symptomatic improvement; therapy design; treatment response

Abstract Major depressive disorder (MDD) is a common, debilitating illness, and new treatments are desperately needed. Addressing this critical need, this exploratory R21 proposal will use real-time fMRI neurofeedback (rtfMRI-NF), which has demonstrated that individuals can self-modulate brain activity. Training patients with depression with rtfMRI-NF paradigms has demonstrated promising, but modest, improvement in depressive symptoms, and new refinements in targeting key brain networks should generate better therapeutic leverage. fMRI studies of large-scale networks (LSN) in the brain have identified aberrant connectivity within and between networks in MDD, as well as other psychiatric disorders. Accordingly, this proposal will develop a new rtfMRI-NF paradigm that specifically targets interactions between two large-scale networks, critical for healthy psychological functioning – the salience network (SN) and the default-mode network (DMN). A fundamental observation of LSNs is that the DMN, activated by internally generated content, deactivates during tasks requiring attention to external stimuli. The SN, along with other ‘task positive’ networks, exhibit a reciprocal relationship with the DMN, becoming active in tasks requiring external direction of attention and executive functions when the DMN deactivates. Nodes within the SN, such as the anterior insula, have been suggested to mediate the balance between the DMN and task positive networks. As imbalanced connectivity amongst these networks is linked to depression, methods to boost SN function may improve therapeutic responses. For the first step in a larger program to test this hypothesis, this proposal will develop a ’Recall Stop Task’ (RST), using rtfMRI-NF, to target “switching off” the DMN and activating the SN. Forty patients with active MDD will be randomized in a double-blinded, controlled study. In Aim 1, during an initial (localizer) MRI session, network participation in this network switching task will be evaluated, testing the hypothesis that SN is engaged by the RST. For Aim 2, participants will be randomized to receive either valid NF from the personalized network activated by the switching task during the localizer session, or they will receive sham NF. We will test the hypothesis that real, compared to sham NF, will increase activation in the RST, and that the NF task will increase SN connectivity. Successful development of this switching network NF paradigm will provide a robust, neuroscience-informed target for personalized interventions designed to engage networks at the level of the individual subject. The next step would entail an R61/R33 project to demonstrate target engagement and test the hypothesis that symptom amelioration for MDD will follow. Although subsequent steps will focus on MDD, benefits are predicted to be transdiagnostic.

摘要 严重抑郁障碍(MDD)是一种常见的、使人虚弱的疾病，新的治疗方法正在不顾一切地涌现 需要的。为了满足这一关键需求，这一探索性的R21提案将使用实时功能磁共振神经反馈 (rtfMRI-NF)，它已经证明个人可以自我调节大脑活动。对患者进行培训 应用rtfMRI-NF范例治疗抑郁症显示出有希望的，但幅度不大的抑郁症改善 症状，以及针对关键大脑网络的新改进，应该会产生更好的治疗作用。 对大脑中大规模网络(LSN)的功能磁共振研究发现，大脑和大脑之间存在异常连接 在MDD网络之间，以及其他精神疾病之间。因此，这项建议将制定一种新的 RtfMRI-NF范式，专门针对两个大规模网络之间的交互，对健康至关重要 心理功能--显著网络(SN)和默认模式网络(DMN)。一项基本原则 对LSN的观察是，由内部生成的内容激活的DMN在任务期间停用 需要注意外部刺激的。SN与其他“任务积极”网络一起，呈现出一种互惠的关系 与DMN建立关系，在需要外部关注和执行的任务中发挥积极作用 DMN停用时起作用。已经提出了SN内的节点，如前岛 调节DMN和任务积极网络之间的平衡。因为不平衡的连接性 这些网络与抑郁症有关，增强SN功能的方法可能会改善治疗反应。为 测试这一假说的更大程序的第一步，这一提议将开发一个“召回停止任务”(RST)， 使用rtfMRI-nF，靶向“关闭”DMN并激活SN。40名活动期MDD患者将被 在双盲对照研究中随机进行。在目标1中，在初始(本地化)MRI会话期间，网络 将评估对此网络切换任务的参与，测试SN由 RST。对于目标2，参与者将被随机地从个性化网络接收有效的NF 在定位器会话期间由切换任务激活，否则它们将收到假的NF。我们将测试 假设与假核素相比，真实核素将增加RST中的激活，并且核素任务将 增加SN连接。这种交换网络NF范例的成功开发将提供一种稳健的、 神经科学知情的个性化干预目标，旨在使网络参与到 个人主题。下一步将需要一个R61/R33项目来展示目标参与度和测试 假设MDD的症状将会改善。尽管后续步骤将侧重于MDD， 据预测，这些好处是跨诊断的。