Advanced Modeling for Adaptive DBS: Capturing Parkinson’s Disease Complexity

Parkinson’s Disease (PD) represents a significant challenge due to its complex symptomatology and the diminishing efficacy of pharmacological treatments over time. Deep Brain Stimulation (DBS) has emerged as a viable alternative, particularly through its adaptive variant (aDBS), which aims to modulate neural activity via feedback-controlled stimulation. However, the development of aDBS is hindered by oversimplified disease models that fail to capture the nuanced dynamics of the disease observed in patients. This paper addresses the need for more realistic PD models that integrate comprehensive physiological and neural dynamics to enhance aDBS training and efficacy. We present a conceptual framework based on the Kuramoto model, enriched with modifications to incorporate spatial, temporal, and frequency characteristics of PD-related neural activity. Spatial features include localized stimulation and recording capabilities reflective of advanced electrode technologies. Temporarily, the model accounts for the highly variable and stochastic nature of neural activity, essential for realistic simulations of neural behavior. Frequency aspects are detailed to match the neuro-biologically observed spectra in PD patients, focusing on the beta frequency band’s role in symptom manifestation. Through integrating these dimensions into a cohesive model, we aim to refine aDBS approaches and improve their clinical outcomes by providing a more realistic simulation environment for algorithm development and testing. This work lays the groundwork for future research on the intersection of neuroengineering and clinical neuroscience, promising to advance the adaptability and precision of therapeutic interventions for PD and potentially other neurological conditions.