Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a standard treatment for advanced Parkinson’s disease (PD). The precise positioning of the electrode can significantly influence the results of DBS and the overall improvement in the quality of life for PD patients receiving this therapy. We hypothesize that single unit activity (SUA) features can serve as a valid marker of the optimal DBS-electrode insertion trajectory, leading to the most favorable outcome of STN-DBS surgery. We analyzed spontaneous SUA data recorded during Microelectrode Recording (MER) for 21 patients with PD who underwent DBS surgery. We compared 29 linear and 6 nonlinear characteristics of the STN neural activity recorded along different microelectrode insertion paths to determine features corresponding to favorable stimulation outcomes. Our research indicated that the SUA features of pause neurons in a dorsal STN region significantly affected stimulation outcomes. For the trajectories chosen for lead insertion, firing rate, burst rate, and oscillatory activity at 8-12 and 12-20 bands were significantly decreased. Moreover, nonlinear feature analysis showed a significant increase in mutual information for the chosen trajectories. Our findings highlight the significance of specific indicators, such as the activity of pause neurons in the dorsal region and numerous linear SUA characteristics, in determining the optimal lead installation trajectory. Furthermore, our findings emphasize the importance of investigating paths rejected during test-stimulation to understand motor impairment in Parkinson’s disease and its treatment mechanisms.

Excessive beta oscillations in the subthalamic nucleus are established as a primary electrophysiological biomarker for motor impairment in Parkinson’s disease and are currently used as feedback signals in adaptive deep brain stimulation systems. However, there is still a need for optimization of stimulation parameters and the identification of optimal biomarkers that can accommodate varying patient conditions, such as ON and OFF levodopa medication. The precise boundaries of “pathological” oscillatory ranges, associated with different aspects of motor impairment, are still not fully clarified. In this study, we hypothesized that analyzing periodic and aperiodic components of subthalamic nucleus activity separately and identifying functionally distinct subranges within 8-35 Hz based on oscillatory properties may reveal robust biomarkers for specific aspects of motor impairment. We analyzed subthalamic nucleus activity of 14 patients with Parkinson’s disease. Local field potentials were recorded at rest from externalized electrodes postoperatively, both before and after levodopa administration. We showed that levodopa administration suppressed oscillations across a broad frequency range (11-32 Hz) and increased the slope of the aperiodic component. Changes in the aperiodic slope correlated with motor symptom alleviation. Periodic activity was linked to motor symptom severity: peak amplitude within the 14-20 Hz range correlated with overall motor impairment in the OFF state, while the 7-11 Hz range was associated with bradykinesia in the ON state. Our findings suggest that, in addition to low beta, alpha oscillations and the aperiodic component may serve as promising biomarkers for motor impairment and potential feedback signals in adaptive DBS systems.

Movement disorders such as Parkinson’s disease (PD) and cervical dystonia (CD) are associated with abnormal neuronal activity in the globus pallidus internus (GPi). Reduced firing rate and presence of spiking bursts are typical for CD, while PD is characterized by high frequency tonic activity. This research aims to identify the most important pallidal spiking parameters to classify these conditions. We analyzed the single unit activity of the external (GPe) and internal (GPi) segments of the globus pallidus in 11 CD and 10 PD patients who underwent standard DBS implantation. We compared firing rate, firing pattern and oscillatory characteristics of tonic, burst and pause cells and used logistic regression and random forest models to classify patients according to their pallidal activity. In the GPi we discovered prevalence of high firing rate tonic cells in patients with PD, while in dystonia burst neurons with high firing rate were predominant. GPi pause cells were mostly observed in CD patients and exhibited less spike variability compared to PD. Characteristics of neurons and their distribution in the GPe was similar. Logistic regression and random forest models identified spike variability and randomness as the key features for distinguishing between PD and CD, instead of firing rate or oscillation properties. Our study demonstrates that pallidal activity can predict Parkinson’s disease and cervical dystonia with high accuracy. Burst dynamics and characteristics of spiking randomness including entropy appear to be the most meaningful reflections of the neurophysiology of studied diseases.