Parkinson’s Disease (PD) is the second most common neurodegenerative disorder, yet significant Heterogeneity remains in how the disease is subtyped using both clinical and data-driven methods. Most commonly, patients are grouped based on Motor Symptom Profiles into categories such as Tremor-Dominant (TD), Akinesia-Rigidity (AR), and Postural Instability and Gait Disorder (PIGD). Each phenotype is associated with distinct Disease Progression, Symptom Severity, and Clinical Characteristics, and emerging evidence suggests that response to Deep Brain Stimulation (DBS) may also vary by phenotype. To address this variability, our research integrates peri-operative electrophysiology (e.g., local field potentials [LFP], single-unit activity [SUA], EEG), Neuroimaging (e.g., fMRI), advanced Signal Processing, Machine Learning, and Neuromodulation techniques. Our goal is to characterize the Neural Signatures of PD phenotypes and to support the development of more Personalized and Adaptive DBS Therapies, ultimately improving clinical outcomes and quality of life for individuals with PD.
