Dystonia is characterized by abnormal twisting and turning of body parts, sometimes accompanied by tremulous movements, while tremor involves rhythmic oscillatory movements. These phenomena can coexist, particularly in forms of dystonia that resemble tremor, known as jerky dystonia. This study investigates the physiological differences in pallidal neurons among patients with jerky dystonia, tremor with dystonia, and their combination (mixed dystonia), and identifies neuronal characteristics that predict therapeutic success with deep brain stimulation (DBS). Our analysis of neuronal activity in patients undergoing DBS therapy revealed distinct patterns based on therapeutic effects. In the ’No Effect’ group, neurons had similar characteristics across jerky dystonia, dystonia with tremor, and mixed subgroups, with significant differences in firing rate and preburst interval. The ’Good Effect’ group showed more pronounced differences, with higher firing rates and lower preburst intervals in jerky dystonia compared to dystonia with tremor and mixed dystonia. Dystonia with tremor had higher burst spike percent and longer preburst intervals, while mixed dystonia had the highest preburst interval. These findings indicate that jerky dystonia and tremor with dystonia involve distinct physiological processes, characterized by different neuronal subtypes and firing responses. Mixed dystonia represents a unique physiological process, not merely a combination of the other two. The regions of the pallidum that improve jerky dystonia and tremor are anatomically different. This suggests distinct connectivity patterns and has practical implications for predicting therapeutic success with DBS in different dystonia subtypes.

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.