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Background: Anxiety is one of the most common and disturbing non-motor symptoms of Parkinson's disease (PD). However, few studies have explored the relationship between functional connectivity (FC) and the rate of anxiety improvement after subthalamic nucleus deep brain stimulation (STN-DBS). Therefore, in this study, we aimed to explore the correlation between FC and the rate of anxiety improvement in patients with PD who underwent STN-DBS., Methods: The resting-state functional magnetic resonance imaging (rs-fMRI) data of 62 patients with anxious PD (aPD), 68 patients with PD without anxiety (naPD), and 64 healthy controls (HCs) were analyzed according to FC. Intergroup comparison and correlation analyses of anxiety improvement rates were performed., Results: The HC, aPD and naPD groups of zFCs were then used for the ANOVA test, and the results were FDR-corrected. There were 24 significant differences in FCs between the three groups. Post tests were conducted between groups found that 15 significantly different FCs were observed between the naPD and aPD groups. In addition, the two FCs in patients with aPD were significantly correlated with the rate of improvement in anxiety., Conclusion: We found that the two FCs in patients with aPD (olfactory cortex and inferior frontal gyrus [IFG] pars orbitalis; inferior temporal gyrus and posterior orbital gyrus) were significantly correlated with the rate of improvement in anxiety. Our study may help us understand the underlying mechanisms by which STN-DBS improves anxiety in PD patients and identify more effective treatment strategies., Competing Interests: Bowen Chang and Jiaming Mei are co-first authors for this study. The authors report no conflicts of interest concerning the materials or methods used in this study or the findings specified in this paper., (© 2023 Chang et al.)

Objective Deep brain stimulation (DBS) for treatment of advanced Parkinson's disease (PD) has two anatomical targets: the subthalamic nucleus (STN) and the globus pallidus internus (GPI). The clinical effectiveness of these two stimulation targets was compared in the present study. Methods A systematic review and meta-analysis was performed to evaluated the postoperative changes in the United Parkinson's Disease Rating Scale (UPDRS) on- and off-phase, on-stimulation motor scores; activities of daily living score (ADLS); and levodopa equivalent dose (LED) after STN and GPI stimulation. Randomized and nonrandomized controlled trials of PD treated by STN and GPI stimulation were considered for inclusion. Results Eight published reports of eligible studies involving 599 patients met the inclusion criteria. No significant differences were observed between the STN and GPI groups in the on-medication, on-stimulation UPDRS motor score [mean difference, 2.15; 95% confidence interval (CI), -0.96-5.27] or ADLS (mean difference, 3.40; 95% CI, 0.95-7.76). Significant differences in favor of STN stimulation were noted in the off-medication, on-stimulation UPDRS motor score (mean difference, 1.67; 95% CI, 0.98-2.37) and LED (mean difference, 130.24; 95% CI, 28.82-231.65). Conclusion The STN may be the preferred target for DBS in consideration of medication reduction, economic efficiency, and motor function improvement in the off phase. However, treatment decisions should be made according to the individual patient's symptoms and expectations.

Background: Uric acid is a natural antioxidant and it has been shown that low levels of uric acid may be a risk factor for the development of Parkinson's disease. We aimed to investigate the relationship between uric acid and improvement of motor symptoms in patients with Parkinson's disease after subthalamic nucleus deep brain stimulation. Methods: We analyzed the correlation between serum uric acid levels in 64 patients with Parkinson's disease and the rate of improvement of motor symptoms 2 years after subthalamic nucleus deep brain stimulation. Results: A non-linear correlation was observed between uric acid levels and the rate of motor symptom improvement after subthalamic nucleus deep brain stimulation, during both the drug-off and drug-on periods. Conclusions: Uric acid is positively associated with the rate of motor symptom improvement in subthalamic nucleus deep brain stimulation within a certain range. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease (PD) represents one of the most frequently seen neurodegenerative disorders, while anxiety accounts for its non-motor symptom (NMS), and it has greatly affected the life quality of PD cases. Bilateral subthalamic nucleus deep brain stimulation (STN-DBS) can effectively treat PD. This study aimed to develop a clinical prediction model for the anxiety improvement rate achieved in PD patients receiving STN-DBS. Methods: The present work retrospectively enrolled 103 PD cases undergoing STN-DBS. Patients were followed up for 1 year after surgery to analyze the improvement in HAMA scores. Univariate and multivariate logistic regression were conducted to select factors affecting the Hamilton Anxiety Scale (HAMA) improvement. A nomogram was established to predict the likelihood of achieving anxiety improvement. Receiver operating characteristic (ROC) curve analysis, decision curve analysis (DCA), and calibration curve analysis were conducted to verify nomogram performance. Results: The mean improvement in HAMA score was 23.9% in 103 patients; among them, 68.9% had improved anxiety, 25.2% had worsened (Preop) anxiety, and 5.8% had no significant change in anxiety. Education years, UPDRS-III preoperative score, and HAMA preoperative score were independent risk factors for anxiety improvement. The nomogram-predicted values were consistent with real probabilities. Conclusions: Collectively, a nomogram is built in the present work for predicting anxiety improvement probability in PD patients 1 year after STN-DBS. The model is valuable for determining expected anxiety improvement in PD patients undergoing STN-DBS. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease is a common neurodegenerative disease, with depression being a common non-motor symptom. Bilateral subthalamic nucleus deep brain stimulation is an effective method for the treatment of Parkinson's disease. Thus, this study aimed to establish a nomogram of the possibility of achieving a better depression improvement rate after subthalamic nucleus deep brain stimulation in patients with Parkinson's disease. Methods: We retrospectively analyzed 103 patients with Parkinson's disease who underwent subthalamic nucleus deep brain stimulation and were followed up for the improvement of their Hamilton Depression scale scores 1 year postoperatively. Univariate and multivariate logistic regression analyses were used to select factors affecting the improvement rate of depression. A nomogram was then developed to predict the possibility of achieving better depression improvement. Furthermore, the discrimination and fitting performance was evaluated using a calibration diagram, receiver operating characteristics, and decision curve analysis. Results: The mean and median improvement rates of Hamilton Depression scores were 13.1 and 33.3%, respectively. Among the 103 patients, 70.8% had an improved depression, 23.3% had a worsened depression, and 5.8% remained unchanged. Logistic multivariate regression analysis showed that age, preoperative Parkinson's Disease Questionnaire, Hamilton Anxiety, and Hamilton Depression scores were independent factors for the possibility of achieving a better depression improvement rate. Based on these results, a nomogram model was developed. The nomogram had a C-index of 0.78 (95% confidence interval: 0.69–0.87) and an area under the receiver operating characteristics of 0.78 (95% confidence interval: 0.69–0.87). The calibration plot and decision curve analysis further demonstrated goodness-of-fit between the nomogram predictions and actual observations. Conclusion: We developed a nomogram to predict the possibility of achieving good depression improvement 1 year after subthalamic nucleus deep brain stimulation in patients with Parkinson's disease, which showed a certain value in judging the expected depression improvement of these patients. [ABSTRACT FROM AUTHOR]

Objective: Programming plays an important role in the outcome of deep brain stimulation (DBS) for Parkinson's disease (PD). This study introduced a new application for functional zonal image reconstruction in programming. Methods: Follow-up outcomes were retrospectively compared, including first programming time, number of discomfort episodes during programming, and total number of programming sessions between patients who underwent image-reconstruction-guided programming and those who underwent conventional programming. Data from 142 PD patients who underwent subthalamic nucleus (STN)-DBS between January 2017 and June 2019 were retrospectively analyzed. There were 75 conventional programs and 67 image reconstruction-guided programs. Results: At 1-year follow-up, there was no significant difference in the rate of stimulus improvement or superposition improvement between the two groups. However, patients who underwent image reconstruction-guided programming were significantly better at the first programming time, number of discomfort episodes during programming, and total number of programming sessions than those who underwent conventional programming. Conclusion: Imaging-guided programming of directional DBS leads was possible and led to reduced programming time and reduced patient side effects compared with conventional programming. [ABSTRACT FROM AUTHOR]

Purpose: Parkinson's disease (PD) is a common neurodegenerative disease, for which cognitive impairment is a non-motor symptom (NMS). Bilateral subthalamic nucleus deep brain stimulation (STN-DBS) is an effective treatment for PD. This study established a nomogram to predict cognitive improvement rate after STN-DBS in PD patients. Methods: We retrospectively analyzed 103 PD patients who underwent STN-DBS. Patients were followed up to measure improvement in MoCA scores one year after surgery. Univariate and multivariate logistic regression analyses were used to identify factors affecting improvement in cognitive status. A nomogram was developed to predict this factor. The discrimination and fitting performance were evaluated by receiver operating characteristics (ROC) analysis, calibration diagram, and decision curve analysis (DCA). Results: Among 103 patients, the mean improvement rate of the MoCA score was 37.3% and the median improvement rate was 27.3%, of which 64% improved cognition, 27% worsened cognition, and 8.7% remained unchanged. Logistic multivariate regression analysis showed that years of education, UPDRSIII drug use, MoCA Preop, and MMSE Preop scores were independent factors affecting the cognitive improvement rate. A nomogram model was subsequently developed. The C-index of the nomogram was 0.98 (95%CI, 0.97–1.00), and the area under the ROC was 0.98 (95%CI 0.97–1.00). The calibration plot and DCA demonstrated the goodness-of-fit between nomogram predictions and actual observations. Conclusion: Our nomogram could effectively predict the possibility of achieving good cognitive improvement one year after STN-DBS in patients with PD. This model has value in judging the expected cognitive improvement of patients with PD undergoing STN-DBS. [ABSTRACT FROM AUTHOR]

Background: Main motor characteristics in Parkinson's disease (PD) include bradykinesia, rigidity, and tremors. With the development of neuromodulation techniques, it has become possible to use deep brain stimulation (DBS) to control the symptoms of PD. However, since the subthalamic nucleus(STN) and globus pallidus interna (GPi) DBS have their own advantages and disadvantages, it is difficult to control symptoms of the patients. It is essential to find new stimulation methods. Case Presentation: A 33-year-old male PD patient with onset at the age of 12 years. The onset of the disease is presented with bradykinesia and progressively developed severe choreic dyskinesia with the use of medications. We then performed a thorough evaluation of the patient and decided to perform bilateral globus pallidus interna combined with subthalamic nucleus variable frequency DBS (bSGC-DBS) implantation, and after 2 years of follow-up the patient's bradykinesia and dyskinesia symptoms and quality of life improved significantly. Conclusions: This is the first case of bSCG-DBS in a PD patient with refractory dyskinesia, and the first report of encouraging results from this clinical condition. This important finding explores multi-electrode and multi-target stimulation for the treatment of dystonia disorders. [ABSTRACT FROM AUTHOR]

Accurate preoperative path planning plays an essential role in a neurosurgical procedure of deep brain stimulation, leading to a successful procedure with significant surgical outcomes. Conventional preoperative path planning is time-consuming and uncertain, depending highly on the knowledge and experience of the clinician who has to manually plan the electrode-implant path. This work presents a new preoperative path planning strategy for neurostimulation to automatically and accurately find the optimal electrode-implant trajectory. Specifically, a coarse-to-refine neural network model is proposed to accurately segment anatomical brain structures such as the subthalamic nucleus, while the path planning is formulated as an optimization task that minimizes the surgical risk on the implantation trajectory through the segmented brain structures, as well as ensures the puncture path at the safest distance to targets of interest in the brain. We evaluate our method on retrospective neurostimulation data and compare it to the puncture path generated by experienced surgeons, with the experimental results showing that our method provides surgeons with automatic and accurate electrode implant trajectory comparable or even better than manual planning of fellow surgeons. • A coarse-to-refine deep learning segmentation method to extract the target and risk structures in 3D MRI data for the DBS planning. • A safe and optimized puncture trajectory to mimic surgeons for image guided preoperative path planning with 3D MRI data. • Evaluation of automatic puncture trajectory planning on postoperative 3D CT data. [ABSTRACT FROM AUTHOR]