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 重度抑郁症（MDD）是一种常见的，使人衰弱的疾病，新的治疗方法正在拼命 needed.为了满足这一关键需求，这项探索性的R21提案将使用实时功能磁共振成像神经反馈 （rtfMRI-NF），其已经证明个体可以自我调节大脑活动。培训患者， 使用rtfMRI-NF范式治疗抑郁症已经证明了有希望的，但适度的，抑郁症的改善。 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效果。 对大脑中大规模网络（LSN）的功能磁共振成像研究已经确定了大脑内部的异常连接， 以及其他精神疾病之间的联系。因此，该提案将制定一项新的 rtfMRI-NF范式，专门针对两个大规模网络之间的相互作用，对健康至关重要 心理功能-突出网络（SN）和默认模式网络（DMN）。一项基本 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。SN，沿着其他“任务积极”网络，表现出一个互惠的 与DMN的关系，在需要外部注意力和执行力的任务中变得活跃 当DMN停用时，功能。SN内的节点，如前髓，已被建议 调节DMN和任务正性网络之间的平衡。由于不平衡的连通性， 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 作为测试这一假设的更大项目的第一步，该提案将开发一个“回忆停止任务”（Recall Stop Task，简称CSTR）， 使用rtfMRI-NF，以靶向“关闭”DMN并激活SN。40例活动性MDD患者将 在双盲对照研究中随机分组。在目标1中，在初始（定位器）MRI会话期间，网络 将评估参与此网络切换任务的情况，检验SN参与网络切换任务的假设。 - 是的对于目标2，受试者将被随机分配接受来自个性化网络的有效NF 在定位器会话期间由切换任务激活，否则它们将接收伪NF。我们将测试 假设与假NF相比，真实的NF将增加海马区的激活，并且NF任务将 增加SN连接性。这种开关网络NF范例的成功开发将提供一个强大的， 神经科学知情的个性化干预目标，旨在参与网络的水平， 个体主体。下一步将需要一个R61/R33项目来演示目标接合和测试 MDD症状改善的假设。尽管后续步骤将集中在MDD上， 预期益处是跨诊断的。