Characterization of Longitudinal EEG Biomarkers in Chronic Low Back Pain

项目摘要/摘要 慢性下腰痛(CLBP)是一种普遍存在的疾病，影响着全球五分之一的成年人，是 全球残疾的单一最大原因。尽管慢性支气管炎的发病率很高，影响也很不利，但其 治疗方法和机制在很大程度上仍不清楚。预测慢性阻塞性肺疾病症状进展的生物标志物 支持以精确为基础的治疗，并最终帮助减少痛苦。基于大脑的纵向休息- CLBP患者的状态神经成像揭示了预测疼痛时序化的神经网络及其 症状进展。尽管早期的研究结果表明，对大脑网络的测量可以导致 对于预后生物标志物的开发，这些模型对短期随访的预测能力最强。 向上。对不同神经系统的测量可能会带来额外的好处，具有更好的预测能力。 情绪和认知功能障碍在CLBP中很常见，发生在行为和大脑水平， 提供了一个独特的机会来检测基于脑的生物标志物的预后。同样，改进了 脑电(EEG)神经成像策略导致了更高的空间分辨率，使 研究人员克服了经典使用的神经成像方式(例如磁共振)的局限性 成像[MRI]和功能MRI)，例如高成本和有限的可及性。使用纵向脑电，这是 以病人为中心的研究项目将提供情绪、认知和 CLBP患者的静息状态网络，将有助于预测CLBP的症状进展。 通过这个指导职业发展奖(K23)，我将使用现代脑电源分析策略来 跟踪基线、3个月和6个月随访的生物标志物及其与疼痛和 情绪和认知功能障碍。在目标1中，我将识别和描述静息状态下的差异， ClpB患者和年龄/性别匹配的对照组之间的情感和认知网络。在《目标2》中，我会 确定不同时间内受试者的变化及其与临床症状的关系。在Aim 3中，作为一个 探索性目标，我将应用机器和深度学习策略来检测全面的签名 CLBP使用来自静息状态、情绪和认知网络的脑电特征。在整个颁奖期间， 我将发展新的和先进的技能来理解CLBP及其共病以及脑电信号- 处理策略、机器/深度学习算法、职业发展和助学金撰写。至 为了完成拟议的学习和培训，我聚集了一支世界级的疼痛成像专家团队， 作为生理学、心理学、脑电图学和统计学学习的导师。这次培训将建立在我以前的基础上 在心理生理学方面的经验，以实现我的长期目标，成为一名专注于R01资助的研究员 以病人为中心的慢性疼痛和心理生理学研究。

Project Summary/Abstract Chronic low back pain (CLBP) is a pervasive disorder affecting up to one-fifth of adults globally and is the single greatest cause of disability worldwide. Despite the high prevalence and detrimental impact of CLBP, its treatments and mechanisms remain largely unclear. Biomarkers that predict symptom progression in CLBP support precision-based treatments and ultimately aid in reducing suffering. Longitudinal brain-based resting- state neuroimaging of patients with CLBP has revealed neural networks that predict pain chronification and its symptom progression. Although early findings suggest that measurements of brain networks can lead to the development of prognostic biomarkers, the predictive ability of these models is strongest for short-term follow- up. Measurements of different neural systems may provide additional benefits with better predictive power. Emotional and cognitive dysfunction is common in CLBP, occurring at the behavioral and cerebral level, presenting a unique opportunity to detect prognostic brain-based biomarkers. Likewise, improvements in electroencephalogram (EEG) neuroimaging strategies have led to increased spatial resolution, enabling researchers to overcome the limitations of classically used neuroimaging modalities (e.g., magnetic resonance imaging [MRI] and functional MRI), such as high cost and limited accessibility. Using longitudinal EEG, this patient-oriented research project will provide a comprehensive neural picture of emotional, cognitive, and resting-state networks in patients with CLBP, which will aid in predicting symptom progression in CLBP. Through this mentored career development award (K23), I will use modern EEG source analysis strategies to track biomarkers at baseline and 3- and 6-month follow-ups and their covariance with markers for pain and emotional and cognitive dysfunction. In Aim 1, I will identify and characterize differences in resting-state, emotional, and cognitive networks between patients with CLPB and age/sex-matched controls. In Aim 2, I will identify within-subject changes across time and their relationship with clinical symptoms. In Aim 3, as an exploratory aim, I will apply machine- and deep-learning strategies to detect a comprehensive signature of CLBP using EEG features from resting-state, emotional, and cognitive networks. Throughout the award period, I will develop new and advanced skills in understanding CLBP and its comorbidities as well as in EEG signal- processing strategies, machine-/deep-learning algorithms, career development, and grant writing. To accomplish the proposed study and training, I have gathered a world-class team of experts in pain imaging, physiology, psychology, EEG, and statistical learning as mentors. This training will build on my prior experience in psychophysiology to achieve my long-term goal of becoming an R01-funded investigator focused on patient-oriented research in chronic pain and psychophysiology.