Characterization of Longitudinal EEG Biomarkers in Chronic Low Back Pain

慢性腰痛的纵向脑电图生物标志物的表征

• 批准号: 10724084
• 负责人: Edward W. Lannon
• 金额: $ 11.51万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-10 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10724084
• 关键词: Adult; Affect; Age; Award; Behavioral; Biological Markers; Brain; Cerebrum; Chronic low back pain; Clinical; Cognitive; Development; Disease; Electroencephalography; Emotional; Functional Magnetic Resonance Imaging; Funding; Future; Goals; Grant; High Prevalence; Image; Impaired cognition; K-Series Research Career Programs; Machine Learning; Magnetic Resonance Imaging; Measurement; Mentors; Modality; Modeling; Modernization; Pain; Patients; Physiology; Process; Prognostic Marker; Psychology; Psychophysiology; Research Personnel; Research Project Grants; Resolution; Rest; Signal Transduction; Source; Symptoms; System; Time; Training; Writing; brain based; brain dysfunction; career development; chronic pain; comorbidity; cost; deep learning; deep learning algorithm; disability; experience; follow-up; improved; learning strategy; neural; neural network; neuroimaging; pain chronification; patient oriented research; predictive marker; prognostic; sex; skills; statistical learning

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.

项目总结/摘要 慢性下腰痛（CLBP）是一种普遍性疾病，影响全球多达五分之一的成年人， 这是世界上最大的残疾原因。尽管CLBP的高患病率和有害影响， 治疗方法和机制仍不清楚。预测CLBP症状进展的生物标志物 支持基于精确的治疗，并最终帮助减少痛苦。纵向脑基础休息- CLBP患者的状态神经影像学显示，神经网络预测疼痛慢性化及其 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 随着预后生物标志物的发展，这些模型的预测能力对于短期随访是最强的。 起来不同神经系统的测量可以提供具有更好预测能力的额外益处。 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，以检测预后脑为基础的生物标志物。同样， 脑电图（EEG）神经成像策略已经导致空间分辨率的增加， 研究人员试图克服传统使用的神经成像模态的局限性（例如，磁共振 成像[MRI]和功能性MRI），如高成本和有限的可及性。使用纵向EEG， 以病人为导向的研究项目将提供一个全面的神经图片的情绪，认知， CLBP患者的静息状态网络，这将有助于预测CLBP的症状进展。 通过这个指导职业发展奖（K23），我将使用现代EEG源分析策略， 跟踪基线和3个月和6个月随访时的生物标志物及其与疼痛标志物的协方差， 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，我将 确定受试者内随时间的变化及其与临床症状的关系。在目标3中， 探索性的目标，我将应用机器和深度学习策略来检测一个全面的签名， CLBP使用来自静息状态、情绪和认知网络的EEG特征。在整个授标期间， 我将发展新的和先进的技能，了解CLBP及其合并症，以及在脑电图信号- 处理策略，机器/深度学习算法，职业发展和赠款写作。到 完成了拟议的学习和培训，我聚集了一个世界级的疼痛成像专家团队， 生理学、心理学、脑电波和统计学习作为导师。这次培训将建立在我之前 在心理生理学的经验，以实现我的长期目标，成为一个R 01资助的调查重点 在慢性疼痛和心理生理学方面以病人为导向的研究。