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）：该项目旨在开发一种新的潜在治疗常见的神经精神障碍，特别是慢性疼痛综合征和情感障碍，包括抑郁症，焦虑症和创伤后应激障碍（PTSD）。我们之前已经表明，实时功能磁共振成像（rt-fMRI）可以提供反馈信号（神经反馈），使人们能够获得主动控制激活的头端前扣带回（rACC），这反过来又改变了疼痛感知。在这里， 我们的目标是开发一种大脑验证的脑电图（EEG）神经反馈方法，包括同时记录EEG和fMRI。然后，该系统将用于确定反映rACC和杏仁核调节的EEG特征。多种精神状态（例如，抑郁、焦虑）与不能调节情绪有关，并且似乎以包括ACC和杏仁核的边缘回路的功能障碍为特征。然而，目前的精神病治疗方法通常缺乏关于大脑网络和机制的特异性。基于研究表明，个人可以通过在线反馈来训练自己的心理和生理反应，拟议的研究旨在开发一种新的工具，通过心理训练，利用大脑信号作为反馈的基础，提高生理弹性。因此，神经反馈可以提供一个潜在的，补充/替代的方法治疗神经精神障碍。同时获得的高空间分辨率的fMRI数据和高时间分辨率的EEG数据与先进的统计学习方法的组合提供了一种合理的方式来推断和生成一个强大的和有效的EEG神经反馈信号，甚至可以针对深部脑结构，如rACC或杏仁核作为反馈的来源。鉴于精神病理学的高患病率和当前治疗的非特异性靶向，开发一种用于特定边缘系统区域的学习调节的稳健方法是重要的。具体来说，新的EEG神经反馈工具可能是一种新颖的，合理的，基于神经科学的治疗干预，可以成为一种便携式，易于使用和低成本的临床工具，用于改善对大脑工作的自我控制。从长远来看，这种技术可能有助于缓解慢性疼痛，调节抑郁症和焦虑症患者的情绪不稳定和痛苦。 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，包括慢性疼痛综合征，抑郁症和焦虑症，这些疾病具有严重的社会和心理后果。该项目旨在开发一种新的系统，使人们能够学会调节疼痛和情绪，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。