fMRI-based Biomarkers for Multiple Components of Pain

描述(申请人提供)：客观的病理生物标记物存在于许多疾病中，它们的发展是现代对抗疗法医学的重大进步之一。然而，对疼痛和其他精神健康障碍的客观评估远远落后。疼痛不能仅用外周损害来解释；它是由各种神经病理过程引起的，这使得评估和治疗变得困难。目前，衡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可以影响报告的疼痛，但它们可能是通过一套不同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，目前的疼痛治疗方法对少数人有效， 给病人和社会带来的代价。身体疼痛的生物标记物可以根据潜在的神经病理而不是外部症状来表征疼痛，从而极大地改善诊断和治疗。他们还可以通过允许针对所涉及的神经病理类型的干预来改进治疗。能够揭示导致疼痛的大脑病理生理学的生物标记物必须依赖于对大脑功能的直接测量。在过去的几年里，功能磁共振成像(FMRI)与机器学习技术相结合的重大进展-在复杂数据集中找到预测模式的算法-已经使基于fMRI的疼痛评估的目标变得触手可及。在初步数据中，我们首次表明，fMRI活动可以预测一个人经历的是高还是低身体疼痛，灵敏度和特异度超过90%。关键的是，与非疼痛的触摸和几类显著的情感事件相比，生物标记物是特定于身体疼痛的。此外，当应用于不同扫描仪和范例的新样本时，它也达到了这种水平的准确度。这一初步的成功提出了一些在基于fMRI的生物标记物可以用于大规模临床试验和临床实践之前必须解决的问题，包括a)跨实验室和程序的健壮性，b)身体部位、方式和疼痛质量的特异性，c)对止痛治疗的反应，以及d)在临床人群中对自发和急性超敏/超敏/痛觉的适用性。在这里，我们建议汇总一个研究人员联盟的现有数据，允许对13项针对健康个体的fMRI研究和18项针对不同临床疼痛人群的研究进行更广泛的敏感性和特异性测试。此外，我们将进行五个新的实验，以解决生物标记物性能的关键方面。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的多个方面的疼痛。

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.