2021 |
Mochizuki, Hideki |
R21Activity Code Description: To encourage the development of new research activities in categorical program areas. (Support generally is restricted in level of support and in time.) |
Development of Neurologic Itch Signature @ University of Miami School of Medicine
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.
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