Development of neurologic itch signature

翻译后摘要：慢性瘙痒是一个全球性的健康问题，影响着全世界数以千万计的人。但 没有客观的生物标志物来评估瘙痒。因为瘙痒是大脑回路活动的结果， 我们建议开发特定的大脑生物标志物来评估疾病状态， 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 现代对抗疗法的巨大进步在瘙痒治疗中，评估瘙痒是重要的 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，这在本质上受到复杂关系的限制， 生物学上的刺激（瘙痒相关的）过程和患者对瘙痒的口头或书面描述之间的关系。在 特别是，自我报告不适用于报告瘙痒能力有限的人，如婴儿， 幼儿和有认知障碍的老年人。解决慢性瘙痒正在成为一个核心 许多皮肤病的发病率和临床试验的主要终点。因此，有一个伟大的 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 感觉。测量这些信号可以成为瘙痒的可靠生物标志物。脑成像研究进展 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， 评估各种心理活动和大脑功能。这一进步，加上低- 成本和高性能的MRI，将扩大功能磁共振成像在医学上的实际应用的可行性。一种大脑活动- 基于瘙痒的生物标志物（即，神经性瘙痒特征，NIS）可能会显着改善 诊断、治疗和临床试验。此外，NIS可能是一种有前途的瘙痒相关生物标志物， 在大脑中的处理，这使得能够更好地了解慢性瘙痒的病理生理学。我们的目标是 研究建议是发展国家创新系统。特别是，我们将证明（1）NIS将选择性地 对瘙痒有反应（即，对疼痛无反应）和（2）NIS不仅可以预测瘙痒的存在， 以及瘙痒强度，因为这些是瘙痒生物标志物的基本要求。为了实现这一目标，我们将 获得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， 功能性MRI（fMRI），并识别瘙痒的特征性脑活动模式（即，国家情报机构）通过分析 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 没有事先的信息。NIS将加速瘙痒研究，提高诊断质量， 治疗瘙痒，这将最终帮助许多人谁患有慢性瘙痒。

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.