NeuralCipher Research overview
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Objective. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an established therapy for Parkinson's disease (PD), yet optimizing outcomes remains challenging due to the complexity of STN functional architecture and the subjectivity of intraoperative assessments. Furthermore, the encoding of fine versus gross movements across STN dorsoventral subregions remains poorly understood. This study aimed to characterize region- and movement-specific modulation of STN neuronal activity using synchronized electrophysiology and markerless motion tracking-derived kinematics.Approach. We recorded intraoperative electrophysiological and videographic data from 11 subjects with PD (15 hemispheres) undergoing awake STN-DBS implantation surgery. Microelectrode recordings were synchronized with high-resolution kinematics during repeated trials of continuous upper-limb motor tasks involving both distal and proximal muscle groups. Instantaneous firing rate (IFR) and neuronal responsiveness metrics were quantified using a parameter-free Zenith of Event-based Time-locked Anomalies (ZETA) statistical framework and analyzed with linear mixed-effects models.Main results. STN single-unit activity (SUA) and multi-unit activity (MUA) features exhibited robust region- and movement-specific modulation. Firing rates for both SUA and MUA were significantly higher in the dorsal STN during active movements, particularly distal fine motor tasks, compared to ventral STN and baseline activity. ZETA-test analyses revealed significant movement-specific responsiveness across STN subregions, with more dextrous, distal movements eliciting stronger responses than more coordinated, proximal movements. Temporal dynamics of neuronal responsiveness also varied with STN depth and movement type, indicating hierarchical recruitment of STN subregions in the encoding of movement-specific temporal motifs.Significance. These findings demonstrate that STN motor representations are context-dependent, anatomically organized, and temporally distinct, reinforcing the STN's dynamic engagement during motor execution. By identifying objective electrophysiological features linked to movement context and STN region, this work provides a quantitative foundation for reducing reliance on subjective assessments to inform data-driven DBS targeting, programming, and closed-loop control strategies.
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