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.

抽象的 重度抑郁症（MDD）是一种常见的，使人衰弱的疾病，新疗法拼命地是 需要。为了满足这一关键需求，此探索性R21提案将使用实时fMRI Neurofeffeack使用 （RTFMRI-NF），这表明个人可以自我调节大脑活动。培训患者 带有RTFMRI-NF范式的抑郁症表现出了令人鼓舞但适中的抑郁症 靶向关键大脑网络的症状以及新的改进应产生更好的治疗杠杆作用。 大脑中大规模网络（LSN）的功能磁共振成像研究已经确定了内部的异常连通性 在MDD中的网络以及其他精神疾病之间。根据该建议将开发新的 RTFMRI-NF范式专门针对两个大型网络之间的相互作用，对健康至关重要 心理功能 - 显着网络（SN）和默认模式网络（DMN）。基本 LSN的观察是，DMN通过内部生成的内容激活，在任务期间停用 需要注意外部刺激。 SN以及其他“任务积极”网络也表现出互惠 与DMN的关系，积极从事需要关注和执行的外部方向的任务 当DMN停用时功能。 SN内的节点（例如前岛）已被建议 调解DMN和任务正面网络之间的平衡。作为不平衡的连通性 这些网络与抑郁症有关，提高SN功能的方法可以改善治疗反应。为了 检验该假设的较大计划的第一步，该建议将开发“召回停止任务”（rst）， 使用RTFMRI-NF，以“关闭” DMN并激活SN。有40例活动MDD患者将是 在双盲，对照研究中随机分组。在AIM 1中，在初始（本地化）MRI会话中，网络 将评估参与此网络交换任务，以测试SN参与的假设 第对于AIM 2，参与者将被随机从个性化网络接收有效的NF 在本地化会话期间通过切换任务激活，或者他们将收到假NF。我们将测试 与假NF相比，真实的假设将增加第一个的激活，并且NF任务将 增加SN连接性。该开关网络NF范式的成功开发将提供强大的 神经科学知识的目标，用于旨在使网络在 个人主题。下一步将需要一个R61/R33项目，以展示目标参与和测试 伴随MDD的症状改善的假设。尽管随后的步骤将集中于MDD，但 预计收益将是经诊断的。