Multimodal Biomarker-Guided Deep Brain Stimulation Programming in Parkinson's Disease: The DBSgram Framework

Deep Brain Stimulation (DBS) is an effective therapy for Parkinson's disease (PD), but clinical programming of stimulation parameters remains a time-consuming process largely guided by subjective symptom assessment. The increasing availability of sensing-enabled neurostimulators and wearable motion sensors provides an opportunity to introduce objective biomarkers into DBS titration. In this work, we present DBSgram, a multimodal framework designed to support data-driven DBS programming by integrating neurophysiological and kinematic measurements acquired during routine clinical titration. The proposed system combines subthalamic nucleus local field potential (STN-LFP) recordings from sensing-enabled neurostimulators with hand kinematic data acquired using wearable inertial measurement units (IMUs). A two-stage synchronization strategy aligns independent data streams from implanted and wearable devices, followed by automated signal processing pipelines for extracting electrophysiological and motor biomarkers. Patient-specific beta-band power is derived from LFP recordings, while tremor, rigidity, and bradykinesia metrics are computed from multi-axis IMU signals using symptom-specific processing algorithms. These synchronized features are then integrated into the DBSgram visualization framework, which maps stimulation amplitude to simultaneous changes in neural activity and objective motor performance. The framework was implemented in a standardized 40-minute clinical titration protocol conducted in a cohort of 18 PD patients implanted with sensing-enabled DBS systems. We present here the analysis of aligned multimodal datasets from different patients to demonstrate proof-of-concept feasibility. The resulting DBSgram visualizations capture stimulation-dependent suppression of pathological beta activity alongside quantitative motor improvements, enabling intuitive identification of patient-specific therapeutic windows. These results demonstrate the technical feasibility of integrating implanted neurophysiological recordings with wearable kinematic sensing during DBS programming. By providing synchronized physiological and motor biomarkers within a unified framework, the DBSgram approach may support more objective and data-driven DBS titration, and contribute to future closed-loop neuromodulation strategies.