In this study the investigators aim to assess the correlates of neurophysiological measures (measurement of brain magnetically evoked response) using DELPHI system. The DELPHI system device is a computerized, electromechanical medical device that produces and delivers non-invasive Transcranial Magnetic Stimulation (TMS) fields to induce electrical currents directed at regions of the cerebral cortex and records the resultant Electroencephalogram (EEG) brain electrophysiological response. DELPHI analyzes the TMS Evoked Potential (TEP) and produces quantitative output measures.
Objectives include:
- To use TMS-evoked EEG measures of brain function in patients with chronic pain using the QuantalX DELPHI system to predict patient specific pain diagnoses using machine learning classification methods.
- To evaluate longitudinal associations between TMS-evoked EEG measures and ratings of chronic pain.
- To monitor associations between TMS-evoked EEG biomarkers and therapy success for three different classes of medications.
Deriving Candidate Diagnostic and Prognostic Network Biomarkers for Chronic Pain Using the QuantalX DELPHI TMS-EEG System
Chronic pain is the leading cause of disability worldwide. Patients with chronic pain have highly variable responses to available treatments, leading to trial-and-error based interventions that delay relief, prolong suffering, and increase reliance on potentially addictive opioid analgesics. This hallmark variability between individual patients is a key barrier to the development of reliable biomarkers for diagnosis and treatment selection. Chronic pain is associated with maladaptive reorganization of brain circuits involved in sensory, emotional, and cognitive aspects of pain. However, specific abnormalities and their relationships to personalized outcomes are unknown. Here, the investigators propose to collect measures of brain network connectivity, excitability, and plasticity using the QuantalX DELPHI-MD (TMS-EEG) system to identify mechanistic biomarkers for patient diagnosis and treatment prognosis. This is a prospective, pilot cohort study. Relationships uncovered during analysis of pilot data will be used to support future experimental research and better characterize specific measures that may be useful to collect in ongoing patient outcome research at UCSF.