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: 8916319
• 负责人: TOR D. WAGER
• 金额: $ 16万
• 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 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8916319
• 关键词: Acute; Address; Affect; Affective; Algorithms; Analgesics; Arthritis; 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; Pelvis; 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; Visual; Work; allodynia; base; chronic pain; clinical practice; 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）：病理学的客观生物标志物存在于许多疾病中，它们的发展是现代对抗疗法医学的巨大进步之一。然而，对疼痛和其他心理健康障碍的客观评估远远落后。疼痛不能仅仅用外周损伤来解释;它是由各种神经病理过程引起的，这使得它难以评估和治疗。目前，测量疼痛的唯一可接受的方法是自我报告，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，但它们可能是通过一组异质的神经生理机制来实现的，对健康和长期福祉有不同的后果。因此，尽管历史悠久， 研究表明，目前的疼痛治疗对少数人有效， 对患者和社会的成本。 身体疼痛的生物标志物可以大大改善诊断和治疗，通过允许疼痛的基础上，潜在的神经病理学，而不是外部症状的特点。它们还可以通过允许针对所涉及的神经病理类型进行干预来改善治疗。 能够揭示导致疼痛的大脑病理生理学的生物标志物必须依赖于对大脑功能的直接测量。在过去的几年里，结合功能性磁共振成像（fMRI）与机器学习技术（在复杂神经网络中寻找预测模式的算法）的重大进展使基于fMRI的疼痛评估的目标触手可及。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或低的身体疼痛，灵敏度和特异性超过90%。重要的是，当与非疼痛触摸和几类突出的情感事件相比时，生物标志物对身体疼痛是特异性的。此外，当跨不同的扫描仪和范例前瞻性地应用于新样本时，它可以达到这种精度水平。 这一初步成功提出了在基于fMRI的生物标志物可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）对身体部位、模态和疼痛质量的特异性，c）对镇痛治疗的反应，以及d）对临床人群中的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，允许在健康个体的13项fMRI研究和不同临床疼痛人群的18项研究中进行更广泛的灵敏度和特异性测试。此外，我们将进行五项新实验，以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，可以评估健康个体和慢性疼痛患者疼痛的多个方面。