fMRI-based Biomarkers for Multiple Components of Pain

基于功能磁共振成像的多种疼痛生物标志物

• 批准号: 8826094
• 负责人: TOR D. WAGER
• 金额: $ 57.82万
• 依托单位: UNIVERSITY OF COLORADO
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8826094
• 关键词: Acute; Address; Affect; Affective; Algorithms; Analgesics; Back; Biological Markers; Brain; Brain Mapping; Clinical; Clinical Research; Clinical Trials; Cognitive; Complex; Data; Data Set; Decision Making; Development; Diagnosis; Disease; Distress; Emotional; Event; Fright; Functional Magnetic Resonance Imaging; Functional disorder; Funding; Goals; Grant; Health; Heating; Human; Hyperalgesia; Hypersensitivity; Image; Individual; Individual Differences; Intervention; Laboratories; Laboratory Procedures; Light; Machine Learning; Measures; Mechanics; Medicine; Mental disorders; Minority; Modality; Modeling; Naproxen; Nerve Pain; Nociception; Pain; Pain Measurement; Pain intensity; Pain quality; Painless; Pathology; Patient Self-Report; Patients; Pattern; Pelvic Pain; Performance; Peripheral; Persons; Physicians; Placebos; Population; Process; Recording of previous events; Reporting; Research; Research Personnel; Sampling; Sensitivity and Specificity; Site; Societies; Specificity; Stimulus; Symptoms; System; Techniques; Testing; Time; Touch sensation; Translating; Treatment Effectiveness; Validation; Visceral pain; Visual; Work; allodynia; arthritic pain; base; chronic pain; clinical practice; cognitive performance; cognitive process; cost; design; distraction; duloxetine; expectation; experience; improved; mental imagery; neuropathology; neurophysiology; painful neuropathy; patient population; psychologic; relating to nervous system; remifentanil; research study; response; social; spontaneous pain; success

DESCRIPTION (provided by applicant): Objective biomarkers of pathology exist for a number of diseases, and their development is one of the great advances of modern allopathic medicine. However, objective assessment of pain and other mental health disorders has lagged far behind. Pain cannot be explained by peripheral damage alone; it is caused by a variety of neuropathological processes, which has made it difficult to assess and treat. Currently, the only acceptable way to measure pain is by self-report, which presents a serious barrier to effective research and treatment. Self-reported pain is influenced by nociceptive, affective, and cognitive decision-making processes-and though there are many treatments that can influence reported pain, they likely do so through a heterogeneous set of neurophysiological mechanisms, with different consequences for health and long-term well being. As a result, in spite of a long history of research, current treatments for pain are effective for a minority of individuals, with enormous costs to patients and to society. Biomarkers for physical pain could dramatically improve diagnosis and treatment, by allowing pain to be characterized on the basis of underlying neuropathology, rather than external symptoms. They could also improve treatment, by allowing interventions to be targeted to type of neuropathology involved. Biomarkers that can shed light on the brain pathophysiology that causes pain must necessarily rely on direct measures of brain function. In the past several years, major advances in combining functional magnetic resonance imaging (fMRI) with machine learning techniques-algorithms for finding predictive patterns in complex datasets-have brought the goal of fMRI-based pain assessment within reach. In preliminary data, we show for the first time that fMRI activity can predict whether an individual person is experiencing high or low physical pain with over 90% sensitivity and specificity. Critically, the biomarker is specific to physical pain when compared with non-painful touch and several classes of salient, affective events. In addition, it achieves this level of accuracy when applied prospectively to new samples, across different scanners and paradigms. This preliminary success raises a number of issues that must be addressed before fMRI-based biomarkers can be used in large-scale clinical trials and clinical practice, including a) robustnes across laboratories and procedures, b) specificity to body site, modality, and quality of pain, c) responses to analgesic treatment, and d) applicability to spontaneous and acute hypersensitivity/allodynia in clinical populations. Here, we propose to aggregate existing data across a consortium of researchers, allowing more extensive tests of sensitivity and specificity across 13 fMRI studies in healthy individuals and 18 studies in diverse clinical pain populations. In addition, we will conduct five new experiments to address critical aspects of biomarker performance. These data will allow us to develop and validate new, more comprehensive biomarkers that can assess multiple aspects of pain across healthy individuals and chronic pain sufferers.

描述（由申请人提供）：许多疾病都存在客观的病理学生物标志物，它们的发展是现代对抗疗法医学的伟大进步之一。然而，对疼痛和其他精神健康障碍的客观评估却远远落后。疼痛不能仅用外周损伤来解释；它是由多种神经病理过程引起的，这使得评估和治疗变得困难。目前，唯一可接受的测量疼痛的方法是自我报告，这对有效的研究和治疗构成了严重障碍。自我报告的疼痛受到伤害性、情感和认知决策过程的影响，尽管有许多治疗方法可以影响报告的疼痛，但它们很可能通过一组异质的神经生理学机制来实现这一点，对健康和长期影响不同。术语“幸福”。因此，尽管历史悠久 根据研究，目前的疼痛治疗方法对少数人有效，但效果巨大 给患者和社会带来成本。 身体疼痛的生物标志物可以根据潜在的神经病理学而不是外部症状来表征疼痛，从而显着改善诊断和治疗。他们还可以通过针对所涉及的神经病理学类型进行干预来改善治疗。 能够揭示引起疼痛的大脑病理生理学的生物标志物必须依赖于大脑功能的直接测量。在过去的几年里，功能性磁共振成像 (fMRI) 与机器学习技术（在复杂数据集中寻找预测模式的算法）相结合的重大进展，使得基于 fMRI 的疼痛评估目标变得触手可及。在初步数据中，我们首次表明功能磁共振成像活动可以预测一个人是否正在经历高强度或低强度的身体疼痛，其敏感性和特异性超过 90%。至关重要的是，与无痛触摸和几类显着的情感事件相比，生物标志物是针对身体疼痛的。此外，当它跨不同的扫描仪和范例前瞻性地应用于新样本时，它可以达到这种水平的准确性。 这一初步成功提出了在基于功能磁共振成像的生物标志物用于大规模临床试验和临床实践之前必须解决的许多问题，包括 a) 跨实验室和程序的稳健性，b) 身体部位、模式和质量的特异性疼痛的影响，c) 对镇痛治疗的反应，以及 d) 对临床人群自发性和急性超敏反应/异常性疼痛的适用性。在这里，我们建议汇总研究人员联盟的现有数据，以便对针对健康个体的 13 项 fMRI 研究和针对不同临床疼痛人群的 18 项研究进行更广泛的敏感性和特异性测试。此外，我们将进行五项新实验来解决生物标志物性能的关键方面。这些数据将使我们能够开发和验证新的、更全面的生物标志物，这些生物标志物可以评估健康个体和慢性疼痛患者疼痛的多个方面。