Objective Integrated Multimodal Electrophysiological Index for the Quantification of Visceral Pain - Project Summary/Abstract Visceral pain is the cardinal complaint of patients with Irritable Bowel Syndrome (IBS), an affliction of approximately 20% of the U.S. population. Drugs to treat visceral pain are limited, with side effects usually outweighing analgesic benefits. The development of effective treatments for IBS is hampered by the lack of a reliable biomarker that quantifies the level of visceral pain. Clinical trials of pain-managing drugs rely on patient reported pain level, a subjective assessment with a strong placebo effect. This has resulted in challenges in clinical trials for the tested drugs to significantly out-perform the placebo in managing visceral pain. Thus, an objective integrated multimodal electrophysiological index that quantifies the level of visceral pain is much needed in developing novel therapies, especially non-drug treatments in managing IBS pain. The autonomic nervous system (ANS) is the primary pathway in brain-gut communication, and it significantly overlaps with the neural circuitry for detecting and perceiving pain from the viscera. Our preliminary data have shown close correlation between ANS activities and subject-reported pain responses to noxious somatic and visceral stimuli. Compared with healthy subjects, IBS patients show an unchecked predominance of sympathetic activities and desensitized parasympathetic activities. Also, the amount of abdominal muscle contraction, measured by electromyogram (EMG), has been established as a metric to differentiate IBS from control populations in preclinical studies. Thus, the ANS and abdominal muscular activities are promising targets for developing objective biomarkers for IBS pain. We aim to leverage our recent technical advances in wearable sensing of electrophysiological signals and telemetric technology to develop a robust, sensitive, and objective biomarker that quantifies the level of visceral pain through noninvasive recordings of electrodermal activities (EDA), electrocardiogram (ECG), and EMG. Two specific aims are proposed. Specific Aim 1 will use machine learning to establish an objective integrated multimodal electrophysiological (OIME) index based on EDA, ECG, and abdominal EMG for pain quantification and clustering of IBS patients. Specific Aim 2 will the integrated OIME index as a biomarker to reliably quantify visceral pain in IBS patients and stratify the IBS population. If successful, we will establish the OIME index from abdominal belt-worn recordings of EDA, ECG, and EMG as a sensitive and robust biomarker for IBS-related visceral pain, which will lead to accelerated development of preventive and therapeutic schemes, especially nonopioid treatment for effective pain management in IBS.
