Chronic pain is a significant problem affecting more than 50 million adult Americans and remains one of the most common reasons that patients seek healthcare. Spinal cord stimulation (SCS) is an FDA-approved neuromodulation treatment to relieve chronic refractory pain. While SCS is widely accepted treatment, there is no objective measurement to assess the effectiveness of SCS in chronic pain patients. Furthermore, there is a high variability in patients’ responses to stimulation with no consensus on optimal stimulation parameters or device selection. Using peri-operative electrophysiology (e.g., EEG, ECG, eye tracking, wearable sensors), signal processing, machine-learning decoding, and neuromodulation (e.g., SCS), we are seeking to identify neural correlates of chronic pain and develop quantified measures that can be used to objectively assess the chronic pain in patients and advance development of new medical technologies.
Parkinson’s disease (PD) is the second most common neurodegenerative disorder. The current evidence shows that there is still a marked heterogeneity in the subtyping of PD using both clinical and data-driven approaches. The most commonly, patients can be divided into groups based on motor symptoms such as tremor dominant (TD), akinesia-rigidity (AR), and postural instability and gait disorder (PIGD). Each phenotype demonstrates different disease progression, symptom severity, and clinical features. Further response to deep brain stimulation (DBS) therapy may also be phenotype dependent. Using peri-operative electrophysiology (e.g., LFP, SUA, EEG), neuroimaging (e.g., fMRI), signal processing, machine-learning decoding, and neuromodulation (DBS), we are working to characterize the neural signatures of PD phenotypes and develop more adaptive therapies.
Studying the disease’s pathophysiology and functional circuitry can shape the electrode technology and new electrode designs can illuminate the different aspects of neural activity resulting in better targeting and improved therapies. Using peri-operative electrophysiology (e.g., EEG, EMG, PF, LFP, SUA), signal processing, machine-learning decoding, wearable sensors, and neuromodulation (e.g., SCS & DBS), we are developing new tools for purposes such as high-resolution functional brain/spine mapping, electrophysiology-guided target localization, improving electrode placement, electrophysiology-guided clinical programming, robust and rapid data visualization, diagnostic and prognostic tools, integrating smart health wearable into clinical decision-making, and optimizing therapy.
More than six million Americans are living with Alzheimer’s disease and related dementias (ADRD), characterized by cognitive and behavioral impairments, and more than 50% of community-dwelling older adults with ADRD experience pain daily. Chronic pain remains mostly untreated in those with ADRD, mainly due to their limited capacity to report pain verbally. As the ability to communicate is often diminished, pain often goes under-recognized and is poorly managed. Development of reliable and objective biomarkers of chronic pain could improve accurate pain assessment and treatment. Thus, using electrophysiology (e.g., wearable EEG, EMG, eye tracking), signal processing, and machine-learning, we aim to identify neural signatures of chronic pain in older adults with early-stage ADRD, in comparison to healthy controls.