Learned regulation of the limbic network via combined EEG and fMRI

通过脑电图和功能磁共振成像的结合学习边缘网络的调节

• 批准号: 8302045
• 负责人: JOHN GABRIELI
• 金额: $ 20.06万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-01 至 2014-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8302045
• 关键词: Address; Adverse effects; Affective; Amygdaloid structure; Anterior; Anxiety; Brain; Brain region; Clinical; Cognitive; Data; Disease; Effectiveness; Electroencephalography; Emotional; Emotions; Face; Feedback; Frequencies; Functional Magnetic Resonance Imaging; Functional disorder; Goals; High Prevalence; Individual; Lead; Learning; Logic; Machine Learning; Magnetic Resonance Imaging; Mental Depression; Mental disorders; Methods; Mood Disorders; Neurosciences; Outcome; Pain; Pattern; Perception; Performance; Physiological; Placebos; Population; Post-Traumatic Stress Disorders; Procedures; Process; Psyche structure; Psychiatric therapeutic procedure; Psychopathology; Regulation; Research; Resolution; Self-control as a personality trait; Signal Transduction; Source; Specificity; Stimulus; Structure; Syndrome; System; Techniques; Testing; Therapeutic Intervention; Time; Training; Validation; Work; base; chronic pain; clinically relevant; clinically significant; common treatment; cost; depressive symptoms; drug development; emotion regulation; improved; innovation; mental state; neurofeedback; neuropsychiatry; novel; psychologic; research study; resilience; response; social; success; therapy development; tool; young adult

DESCRIPTION (provided by applicant): This project aims to develop a novel potential treatment for common neuropsychiatric disorders, specifically chronic pain syndrome and affective disorders including depression, anxiety, and post-traumatic stress disorder (PTSD). We have previously shown that real-time functional magnetic resonance imaging (rt-fMRI) can provide a feedback signal (neurofeedback) that allows people to gain learned voluntary control of activation in the rostral anterior cingulate (rACC), which in turn alters pain perception. Here, we aim to develop a brain-validated electroencephalography (EEG) neurofeedback method that involves simultaneous EEG and fMRI recordings. This system will then be used to determine EEG signatures reflective of regulation of rACC and of the amygdala. Multiple psychiatric states (e.g., depressive, anxious) are associated with an inability to regulate emotion and appear to be characterized by dysfunction of the limbic circuit including the ACC and amygdala. However, current psychiatric treatment approaches typically lack specificity regarding brain networks and mechanisms. Based on research studies indicating that individuals can be trained to regulate their own mental and physiological responses through online feedback, the proposed study aims to develop a novel tool for enhancing physiological resilience through psychological training using brain signals as the basis for such feedback. Thus, neurofeedback can provide a potential, complementary/alternate approach for treatment of neuropsychiatric disturbances. The combination of simultaneously acquired high spatial-resolution fMRI data and high temporal-resolution EEG data together with advanced statistical learning approaches provide a plausible way to infer and generate a robust and effective EEG neurofeedback signals that can target even deep brain structures such as the rACC or amygdala as the source of feedback. Given the high prevalence of psychopathology and the non-specific targeting of current treatments, the development of a robust method for learned regulation of specific limbic regions is important. Specifically, the new EEG-neurofeedback tool could be a novel, rational, neuroscience-based therapeutic intervention that could become a portable, easy-to-use, and low-cost clinical tool for improving self-control over brain working. In the long-term such a technique could be useful in alleviating chronic pain and regulating emotional instability and agony in cases of depression and anxiety syndromes. PUBLIC HEALTH RELEVANCE: The inability to self-regulate our affective/mental states is an underlying cause of multiple disorders, including chronic pain syndrome, depression, and anxiety that have serious social and psychological consequences. This project aims to develop a novel system that enables people to learn to regulate pain and emotion, which could lead to improved outcomes for major neuropsychiatric diseases that are often unresponsive to current treatments.

描述（由申请人提供）：该项目旨在开发针对常见神经精神疾病的新型潜在治疗方法，特别是慢性疼痛综合征和情感障碍，包括抑郁症，焦虑和创伤后应激障碍（PTSD）。我们先前已经表明，实时功能磁共振成像（RT-FMRI）可以提供反馈信号（Neurofeffack），该反馈信号使人们能够在前后扣带回（RACC）中获得对激活的自愿控制（RACC），从而改变了止痛感。这里， 我们旨在开发涉及同时脑电图和fMRI记录的脑验证脑电图（EEG）神经反馈方法。然后，该系统将用于确定反映RACC和杏仁核调节的EEG特征。多种精神病状态（例如，抑郁，焦虑）与无法调节情绪的无法调节，并且似乎以包括ACC和ACC和Amygdala在内的边缘电路功能障碍为特征。但是，当前的精神病治疗方法通常缺乏有关大脑网络和机制的特异性。基于研究表明，可以通过在线反馈对个人进行培训，以调节自己的心理和生理反应，拟议的研究旨在开发一种新颖的工具，通过使用脑信号作为此类反馈的基础，通过心理培训来增强生理韧性。因此，神经反馈可以提供一种治疗神经精神障碍的潜在互补/替代方法。同时获得的高空间分辨率fMRI数据和高时间分辨率的EEG数据以及先进的统计学习方法的结合提供了一种合理的方法，可以推断和产生强大而有效的EEG神经反馈信号，这些信号甚至可以针对诸如RACC或Amygdala之类的深层大脑结构，以反馈。鉴于心理病理学的高流行和当前治疗的非特异性靶向率，用于学习特定边缘区域的可靠方法的发展很重要。具体而言，新型的EEG-否反馈工具可能是一种新颖的，理性的，基于神经科学的治疗干预措施，可以成为一种便携式，易于使用且低成本的临床工具，可改善大脑工作上的自我控制。从长远来看，这种技术可能有助于减轻慢性疼痛，并在抑郁症和焦虑综合症的情况下调节情绪不稳定和痛苦。 公共卫生相关性：无法自我调节我们的情感/精神状态是多种疾病的根本原因，包括慢性疼痛综合症，抑郁症和焦虑，具有严重的社会和心理后果。该项目旨在开发一种新型系统，使人们能够学会调节疼痛和情感，这可能会改善对当前治疗通常无反应的主要神经精神疾病的结果。