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: 8701264
• 负责人: TOR D. WAGER
• 金额: $ 58.93万
• 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/8701264
• 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; public health relevance; 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）：存在多种疾病的病理学生物标志物，它们的发展是现代同种疗法医学的巨大进步之一。但是，对疼痛和其他心理健康障碍的客观评估已远远落后。疼痛不能仅仅通过外围损害来解释；它是由多种神经病理学过程引起的，这使得很难评估和治疗。目前，唯一可以接受的方法是通过自我报告，这为有效的研究和治疗带来了严重的障碍。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会影响报道的疼痛，但它们可能通过一组异质的神经生理机制来影响，但对健康和长期健康的后果都不同。结果，尽管历史悠久 在研究中，目前对疼痛的治疗对少数人有效， 为患者和社会成本。 身体疼痛的生物标志物可以通过基于潜在的神经病理学而不是外部症状来表征疼痛来大大改善诊断和治疗。他们还可以通过允许将干预措施靶向涉及的神经病理学类型来改善治疗方法。 可以阐明导致疼痛的脑病理生理学的生物标志物必须依靠直接测量脑功能。在过去的几年中，将功能磁共振成像（fMRI）与机器学习技术 - 算法相结合的重大进展，用于在复杂数据集中找到预测模式，这使基于功能磁共振成像的疼痛评估的目标触手可及。在初步数据中，我们首次表明fMRI活动可以预测一个人是否患有高或低的身体疼痛，敏感性和特异性超过90％。至关重要的是，与非抚摸触摸和几类显着的情感事件相比，生物标志物特定于身体疼痛。此外，当前瞻性地应用于不同的扫描仪和范式的新样品时，它可以达到这种准确性。 这种初步的成功提出了许多问题，必须在基于fMRI的生物标志物中解决这些问题，可以在大规模的临床试验和临床实践中使用，包括a）跨实验室和程序的鲁棒性，b）身体部位的特异性，对身体的特定性，对疼痛的痛苦，c）对肛门治疗的反应，以及临床杂种症状疗法和急诊疗法。在这里，我们建议在研究人员的一群联盟中汇总现有数据，从而在健康个体中对13个fMRI研究进行更广泛的敏感性和特异性测试，并在各种临床疼痛种群中进行18项研究。此外，我们将进行五个新的实验，以解决生物标志物绩效的关键方面。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。