Development of neurologic itch signature

摘要：慢性瘙痒是一个全球性的健康问题，影响着全球数千万人。然而，在那里 没有客观的生物标记物来评估瘙痒。由于瘙痒是通过参与大脑回路的活动而产生的 在许多大脑区域中，我们建议开发特定的大脑生物标记物来评估疾病状态和 使用脑功能成像和机器学习的治疗效果。生物标志物的发展是 现代对抗疗法的巨大进步。在瘙痒治疗中，对瘙痒的评估是一个重要的 了解慢性瘙痒的进展和治疗效果的指标。目前，瘙痒评估是 几乎完全基于患者的自我报告，这固有地受到复杂关系的限制 在生物瘙痒(瘙痒相关)过程和患者对瘙痒的口头或书面描述之间。在……里面 特别是，自我报告不适用于报告瘙痒能力有限的人，如婴儿，非常 幼儿，以及有认知障碍的老年人。解决慢性瘙痒正在成为一个核心问题 许多皮肤病的发病率和临床试验的主要终点。因此，有一个很大的 需要开发一种可靠的瘙痒生物标志物。瘙痒相关的神经信号是瘙痒的基本要素 轰动一时。测量这些信号可以作为瘙痒的可靠生物标志物。脑成像的最新进展 与机器学习算法相结合，使基于大脑活动的生物标志物的开发成为可能 评估各种精神活动和大脑功能。这一进展，以及正在进行的Low- 性价比高的磁共振成像，将扩大功能磁共振成像在医学上实际应用的可行性。一种大脑活动- 基于瘙痒的生物标志物(即神经瘙痒信号，NIS)可以显著提高患者的治疗质量 诊断、治疗和临床试验。此外，NIS可以作为瘙痒相关的生物标志物 在大脑中进行处理，这使我们能够更好地了解慢性瘙痒的病理生理学。我们的目标是 研究建议是开发国家信息系统。特别是，我们将演示(1)NIS将有选择地 对瘙痒的反应(即，对疼痛不敏感)和(2)NIS不仅可以预测是否存在瘙痒，而且 还有瘙痒强度，因为这些是瘙痒生物标记物的基本要求。为了实现这一目标，我们将 获取在不同强度的瘙痒和疼痛刺激以及休息条件下的大脑活动数据集 功能磁共振成像(FMRI)，并通过分析瘙痒的特征脑活动模式(即NIS) 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程度 在没有事先信息的情况下发痒。NIS将加快瘙痒研究，提高诊断和治疗质量 治疗瘙痒，这最终将帮助许多患有慢性瘙痒的人。

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.