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，迫切需要新的治疗方法 需要。为了满足这一关键需求，这一探索性 R21 提案将使用实时功能磁共振成像神经反馈 （rtfMRI-NF），证明个体可以自我调节大脑活动。对患者进行培训 rtfMRI-NF 范例的抑郁症已被证明有希望但效果有限，可以改善抑郁症 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发现，大脑内部和 MDD 以及其他精神疾病的网络之间的关系。因此，本提案将开发一个新的 rtfMRI-NF范式专门针对两个大型网络之间的交互，这对健康至关重要 心理功能——显着网络（SN）和默认模式网络（DMN）。一个基本的 LSN 的观察结果是，DMN 由内部生成的内容激活，在任务期间停用 需要注意外界刺激。 SN 与其他“任务积极”网络一起，表现出互惠性 与 DMN 的关系，积极参与需要外部关注和执行指导的任务 当 DMN 停用时起作用。 SN 内的节点，例如前岛叶，已被建议 调节 DMN 和任务正向网络之间的平衡。由于之间的连接不平衡 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。为了 作为测试这一假设的更大计划的第一步，该提案将开发一个“召回停止任务”（RST）， 使用 rtfMRI-NF，以“关闭”DMN 并激活 SN 为目标。四十名患有活动性 MDD 的患者将被 在双盲对照研究中随机进行。在目标 1 中，在初始（定位器）MRI 会话期间，网络 将评估 SN 参与此网络交换任务的假设，检验 SN 参与的假设 RST。对于目标 2，参与者将被随机接收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，否则它们将收到假 NF。我们将测试 假设与假 NF 相比，真实 NF 会增加 RST 中的激活，并且 NF 任务将 增加 SN 连接性。这种交换网络 NF 范式的成功开发将提供一个强大的、 基于神经科学的个性化干预目标，旨在在神经网络层面参与网络 个别科目。下一步将需要一个 R61/R33 项目来展示目标参与和测试 MDD 症状随之改善的假设。虽然后续步骤将重点关注 MDD， 预计其益处将是跨诊断的。