Development of neurologic itch signature

神经性瘙痒特征的发展

• 批准号: 10193704
• 负责人: Hideki Mochizuki
• 金额: $ 17.47万
• 依托单位: UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10193704
• 关键词: Address; Affect; Antipruritics; Attention; Biological; Biological Markers; Biological Process; Brain; Brain Pathology; Brain imaging; Brain region; Characteristics; Child; Clinical Trials; Complex; Data Set; Dermatologic; Development; Diagnosis; Disease; Dose; Elderly; Elements; Functional Magnetic Resonance Imaging; Functional disorder; Future; Goals; Hospitals; Human; Impaired cognition; Industry; Infant; Intervention; Machine Learning; Magnetic Resonance Imaging; Measures; Medicine; Modernization; Morbidity - disease rate; Neurologic; Pain; Patient Self-Report; Patients; Pattern; Performance; Physicians; Process; Protocols documentation; Pruritus; Psyche structure; Reporting; Reproducibility; Research; Research Institute; Research Proposals; Rest; Sensitivity and Specificity; Severities; Severity of illness; Signal Transduction; Specificity; Standardization; Stimulus; Testing; Therapeutic; Training; United States National Institutes of Health; Work; base; biomarker development; chronic itch; cost; diagnosis quality; expectation; global health; improved; individual patient; machine learning algorithm; neurotransmission; noninvasive brain stimulation; novel; phrases; primary endpoint; programs; psychologic; success; targeted treatment; treatment effect; treatment trial

Abstract: Chronic itch is a global health problem affecting tens of millions of people worldwide. However, there is no objective biomarker to assess itch. Since itch results from activity in brain circuits through the participation of many brain regions, we suggest developing specific brain biomarkers to assess the disease states and treatment effects using functional brain imaging and machine learning. Developments of biomarkers are one of the great advances of modern allopathic medicine. In itch treatment, assessment of itch is an important indicator in understanding the progress of chronic itch and treatment effect. Currently, itch assessment is based almost exclusively on patients' self-reports, which is inherently limited by the complex relationship between biological pruriceptive (itch-related) processes and patients' verbal or written descriptions of itch. In particular, self-report is not applicable for people who have a limited capacity to report itch such as infants, very young children, and elderly people with cognitive impairments. Addressing chronic itch is becoming a central morbidity in many dermatological diseases and a primary endpoint in clinical trials. Therefore, there is a great need to develop a reliable biomarker for itch. Itch-related neural signals are a fundamental element of the itch sensation. Measuring these signals can be a reliable biomarker for itch. Recent advancement of brain imaging combined with machine learning algorithms has enabled development of brain activity-based biomarkers to assess various mental activities and brain functions. This advancement, together with ongoing progress of low- cost & high-performance MRI, will expand the feasibility of practical use of fMRI in medicine. A brain activity- based biomarker for itch (i.e., Neurologic Itch Signature, NIS) may dramatically improve the quality of diagnoses, treatments and clinical trials. Moreover, the NIS can be a promising biomarker for itch-related processing in the brain, which enables to better understand the pathophysiology of chronic itch. The aim of our research proposal is to develop the NIS. In particular, we will demonstrate (1) that the NIS will selectively respond to itch (i.e., unresponsive to pain) and (2) that the NIS can predict not only an existence of itch but also itch intensity, as these are fundamental requirements of biomarker for itch. To achieve this goal, we will obtain datasets of brain activity during various intensities of itch and pain stimuli and resting condition by using functional MRI (fMRI), and identify a characteristic brain activity pattern for itch (i.e., the NIS) by analyzing the datasets using a machine learning algorithm. We will test whether the created NIS can predict itch and severity of itch without prior information. The NIS will accelerate itch research and improve quality of diagnosis and treatment of itch, which will eventually help the many people who suffer from chronic itch.

摘要：慢性瘙痒是一个全球健康问题，影响了全球数千万人。但是，那里 没有客观的生物标志物评估瘙痒。由于瘙痒是由脑电路的活动引起的 在许多大脑区域中，我们建议开发特定的脑生物标志物来评估疾病状态和 使用功能性脑成像和机器学习的治疗效果。生物标志物的发展是 现代同种疗法医学的巨大进步。在瘙痒治疗中，瘙痒评估是重要的 了解慢性瘙痒和治疗效果的进展的指标。目前，瘙痒评估是 几乎完全基于患者的自我报告，该报告固有地受到复杂关系的限制 在生物学上（瘙痒相关）过程与患者的言语或书面描述之间。在 特别是，自我报告不适用于报告瘙痒能力有限的人，例如婴儿，非常 幼儿和认知障碍的老年人。解决慢性瘙痒正在成为中心 许多皮肤病性疾病的发病率和临床试验的主要终点。因此，有一个很棒的 需要为瘙痒开发可靠的生物标志物。瘙痒相关的神经信号是瘙痒的基本元素 感觉。测量这些信号可能是瘙痒的可靠生物标志物。大脑成像的最新进展 结合机器学习算法使基于大脑活动的生物标志物的发展得以发展 评估各种心理活动和大脑功能。这一进步，以及低 - 成本和高性能MRI将扩大fMRI在医学中实际使用的可行性。大脑活动 - 基于瘙痒的生物标志物（即神经系统瘙痒签名，NIS）可能会显着提高 诊断，治疗和临床试验。此外，NIS可能是与瘙痒有关的有希望的生物标志物 大脑中的处理，这使得能够更好地了解慢性瘙痒的病理生理学。我们的目的 研究建议是开发NIS。特别是，我们将证明（1）NIS将有选择地 回应瘙痒（即对疼痛无反应）和（2）NIS不仅可以预测瘙痒的存在，还可以预测 此外，瘙痒强度是生物标志物对瘙痒的基本要求。为了实现这一目标，我们将 在各种瘙痒和疼痛刺激和静止状态的各种强度时，获得大脑活动的数据集 功能性MRI（fMRI），并通过分析瘙痒（即NIS）的特征性脑活动模式 使用机器学习算法的数据集。我们将测试创建的NIS是否可以预测瘙痒和严重性 没有事先信息的瘙痒。 NIS将加速瘙痒研究并提高诊断的质量和 瘙痒的治疗，最终将帮助许多患有慢性瘙痒的人。