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

基本信息

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
项目类别：
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

项目摘要

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、焦虑和创伤后应激障碍（PTSD）。我们之前已经证明，实时功能磁共振成像（rt-fMRI）可以提供反馈信号（神经反馈），使人们能够获得对头端前扣带回（rACC）激活的习得自愿控制，从而改变疼痛感知。这里，我们的目标是开发一种经过大脑验证的脑电图（EEG）神经反馈方法，该方法涉及同步脑电图和功能磁共振成像记录。然后，该系统将用于确定反映 rACC 和杏仁核调节的脑电图特征。多种精神状态（例如抑郁、焦虑）与无法调节情绪有关，并且似乎以包括 ACC 和杏仁核在内的边缘回路功能障碍为特征。然而，当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。研究表明，可以训练个人通过在线反馈来调节自己的心理和生理反应，该研究旨在开发一种新工具，通过使用大脑信号作为反馈基础的心理训练来增强生理弹性。因此，神经反馈可以为神经精神障碍的治疗提供一种潜在的、补充/替代的方法。同时采集的高空间分辨率功能磁共振成像数据和高时间分辨率脑电图数据与先进的统计学习方法相结合，提供了一种合理的方法来推断和生成强大而有效的脑电图神经反馈信号，该信号甚至可以针对深层大脑结构，例如 rACC或杏仁核作为反馈源。鉴于精神病理学的高患病率和当前治疗的非特异性目标，开发一种强大的方法来学习调节特定边缘区域非常重要。具体来说，新的脑电图神经反馈工具可能是一种新颖、合理、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，用于改善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，调节抑郁和焦虑综合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、抑郁和焦虑，这些疾病会产生严重的社会和心理后果。该项目旨在开发一种新颖的系统，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没有反应的主要神经精神疾病的治疗结果。