Assessing chronic pain using brain entropy mapping

使用脑熵图评估慢性疼痛

• 批准号: 10598873
• 负责人: Ze Wang
• 金额: $ 44.19万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10598873
• 关键词: Address; Adult; Affect; Affective; Anxiety; Area; Attention; Big Data; Biological Markers; Brain; Brain imaging; Brain region; Chronic; Cognitive; Complex; Data; Data Analyses; Disease; Emotional; Ensure; Entropy; Evaluation; Functional Magnetic Resonance Imaging; Future; Goals; Health; Human; Image; Individual; Individual Differences; Intervention; Knowledge; Lead; Limbic System; Location; Machine Learning; Magnetic Resonance Imaging; Maps; Measures; Mental Depression; Methods; Modeling; Monitor; Motor; Motor Cortex; Opioid; Pain; Pain Research; Pain intensity; Pain interference; Pain management; Paper; Parietal Lobe; Perception; Personal Satisfaction; Pharmaceutical Preparations; Pilot Projects; Prefrontal Cortex; Problem Solving; Process; Psyche structure; Regulation; Research; Research Personnel; Rest; Role; Sensorimotor functions; Sensory; Severities; Specificity; Statistical Data Interpretation; Stress; Testing; Time; Translational Research; Trauma; Validation; Work; accurate diagnosis; attentional control; base; biobank; chronic pain; chronic pain patient; cingulate cortex; clinical pain; cognitive control; connectome; deep learning; deep learning model; diagnostic tool; driving force; experience; high reward; human data; indexing; innovation; machine learning model; multitask; neuroimaging; neuromechanism; neuroregulation; novel; pain perception; pain processing; predictive modeling; prognostic tool; success; technique development; therapy development; tool; traumatic stress; treatment effect

Chronic pain is one of the most prevalent health problems in the world but remains poorly understood and challenging to treat or manage. Two major barriers to progress are the unclear brain mechanism of chronic pain and the complexity to model the multifaceted individual differences in pain experience, making it difficult to accurately diagnose pain or monitor pain progression or treatment effects. To solve this problem, we need big data and sophistic models. In this novel project, we will use the large (n~100000) UK Biobank (UKB) data and deep machine learning (DL) to address the two critical problems in chronic pain research. We propose to use resting state fMRI (rsfMRI) because pain perception and processing are ongoing in the brain which can be characterized by the spontaneous brain activity measured by rsfMRI. We will focus on temporal coherence (TC) of rsfMRI given its fundamental role in brain functions and our leading expertise in this research topic. We initiated the concept of brain entropy (BEN) mapping as a tool to measure regional brain TC and our systematic work has demonstrated the high test-retest stability, sensitivity to causal effects, specificity to focal stimulation, different diseases, and drug states, as well as the potential as an intervention target through neuromodulations or medication. We also showed that TC/BEN contains unique information that can not be characterized by other neuroimaging measures. Aim 1 of this project will use TC/BEN mapping to find a potential chronic pain brain circuit where the resting TC is positively correlated with chronic pain and individual differences of pain experience. Aim 2 will use resting TC to build a multi-task DL-based pain prediction model. This project represents the first- of-its-kind to study TC in chronic pain. It will bring new knowledge about chronic pain brain mechanisms (resting TC alterations and associations) and a DL-based quantitative pain prediction model. Research rigor and method/finding generalizability will be ensured by the use of by far the largest rsfMRI data. These high innovations may lead to intervention targets for pain treatment or intervention development and a quantitative tool to evaluate individual differences in pain or pain progression. Feasibility of this project is guaranteed by the existing large data from UKB, our years of work experience in related research fields, the strong pilot data, and the strong team expertise. Success of this pilot project will immediately lead to large size future important studies in this new research direction.

慢性疼痛是世界上最普遍的健康问题之一，但仍然了解不足。 具有挑战性的治疗或管理。进步的两个主要障碍是慢性疼痛的不清楚的大脑机制 以及对痛苦体验中多方面的个体差异进行建模的复杂性，使得难以 准确诊断疼痛或监测疼痛进展或治疗效果。要解决这个问题，我们需要大大 数据和复杂模型。在这个新颖的项目中，我们将使用大型（N〜100000）英国生物银行（UKB）数据和 深度机器学习（DL）解决了慢性疼痛研究中的两个关键问题。我们建议使用 静止状态fMRI（RSFMRI）是因为大脑中正在进行疼痛感知和处理 由RSFMRI测量的自发脑活动的特征。我们将专注于时间连贯性（TC） RSFMRI在大脑功能中的基本作用以及我们在该研究主题中的主要专业知识。我们发起了 大脑熵（BEN）映射的概念是测量区域大脑TC和我们的系统工作的工具 已经证明了高测试稳定性，对因果效应的敏感性，对局灶性刺激的特异性， 不同的疾病和药物状态以及通过神经调节的潜力作为干预措施 或药物。我们还表明，TC/Ben包含的独特信息无法由其他信息来表征 神经影像措施。该项目的目标1将使用TC/BEN映射找到潜在的慢性疼痛大脑 静息TC与慢性疼痛和疼痛经历的个体差异正相关的电路。 AIM 2将使用静息TC构建基于多任务DL的疼痛预测模型。这个项目代表了第一个 在慢性疼痛中研究TC的方法。它将带来有关慢性疼痛脑机制的新知识（休息 TC改变和关联）和基于DL的定量疼痛预测模型。研究严格和 通过使用最大的RSFMRI数据，将确保方法/查找可推广性。这些很高 创新可能会导致疼痛治疗或干预发展的干预目标和定量 评估疼痛或疼痛进展中个体差异的工具。该项目的可行性由 UKB的现有大数据，我们在相关研究领域的工作经验，强大的试点数据和 强大的团队专业知识。该试点项目的成功将立即导致大型未来重要的研究 在这个新的研究方向。