A New Application of Functional Zonal Image Reconstruction in Programming for Parkinson's Disease Treated Using Subthalamic Nucleus-Deep Brain Stimulation

Directional electrodes in deep brain stimulation: Results of a survey by the European Association of Neurosurgical Societies (EANS)

Introduction

Directional Leads (dLeads) represent a new technical tool in Deep Brain Stimulation (DBS), and a rapidly growing population of patients receive dLeads.

Research question

The European Association of Neurosurgical Societies(EANS) functional neurosurgery Task Force on dLeads conducted a survey of DBS specialists in Europe to evaluate their use, applications, advantages, and disadvantages.

Material and methods

EANS functional neurosurgery and European Society for Stereotactic and Functional Neurosurgery (ESSFN) members were asked to complete an online survey with 50 multiple-choice and open questions on their use of dLeads in clinical practice.

Results

Forty-nine respondents from 16 countries participated in the survey (n = 38 neurosurgeons, n = 8 neurologists, n = 3 DBS nurses). Five had not used dLeads. All users reported that dLeads provided an advantage (n = 23 minor, n = 21 major). Most surgeons (n = 35) stated that trajectory planning does not differ when implanting dLeads or conventional leads. Most respondents selected dLeads for the ability to optimize stimulation parameters (n = 41). However, the majority (n = 24), regarded time-consuming programming as the main disadvantage of this technology. Innovations that were highly valued by most participants included full 3T MRI compatibility, remote programming, and closed loop technology.

Discussion and conclusion

Directional leads are widely used by European DBS specialists. Despite challenges with programming time, users report that dLeads have had a positive impact and maintain an optimistic view of future technological advances.

Image-guided programming deep brain stimulation improves clinical outcomes in patients with Parkinson's disease

Deep brain stimulation (DBS) is an effective treatment for patients with Parkinson's disease (PD). However, some patients may not respond optimally to clinical programming adjustments. Advances in DBS technology have led to more complex and time-consuming programming. Image-guided programming (IGP) could optimize and improve programming leading to better clinical outcomes in patients for whom DBS programming is not ideal due to sub-optimal response. We conducted a prospective single-center study including 31 PD patients with subthalamic nucleus (STN) DBS and suboptimal responses refractory to clinical programming. Programming settings were adjusted according to the volumetric reconstruction of the stimulation field using commercial postoperative imaging software. Clinical outcomes were assessed at baseline and at 3-month follow-up after IGP, using motor and quality of life (QoL) scales. Additionally, between these two assessment points, follow-up visits for fine-tuning amplitude intensity and medication were conducted at weeks 2, 4, 6, and 9. After IGP, twenty-six patients (83.9%) experienced motor and QoL improvements, with 25.8% feeling much better and 38.7% feeling moderately better according to the patient global impression scale. Five patients (16.1%) had no clinical or QoL changes after IGP. The MDS-UPDRS III motor scale showed a 21.9% improvement and the DBS-IS global score improved by 41.5%. IGP optimizes STN-DBS therapy for PD patients who are experiencing suboptimal clinical outcomes. These findings support using IGP as a standard tool in clinical practice, which could save programming time and improve patients' QoL.

Functional Connectivity and Anxiety Improvement After Subthalamic Nucleus Deep Brain Stimulation in Parkinson's Disease

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.

Prediction of STN-DBS for Parkinson's disease by uric acid-related brain function connectivity: A machine learning study based on resting state function MRI

Introduction

Parkinson's disease (PD) is a neurodegenerative disorder characterized by dyskinesia and is closely related to oxidative stress. Uric acid (UA) is a natural antioxidant found in the body. Previous studies have shown that UA has played an important role in the development and development of PD and is an important biomarker. Subthalamic nucleus deep brain stimulation (STN-DBS) is a common treatment for PD.

Methods

Based on resting state function MRI (rs-fMRI), the relationship between UA-related brain function connectivity (FC) and STN-DBS outcomes in PD patients was studied. We use UA and DC values from different brain regions to build the FC characteristics and then use the SVR model to predict the outcome of the operation.

Results

The results show that PD patients with UA-related FCs are closely related to STN-DBS efficacy and can be used to predict prognosis. A machine learning model based on UA-related FC was successfully developed for PD patients.

Discussion

The two biomarkers, UA and rs-fMRI, were combined to predict the prognosis of STN-DBS in treating PD. Neurosurgeons are provided with effective tools to screen the best candidate and predict the prognosis of the patient.

Development and Validation of a Prediction Model for Anxiety Improvement after Deep Brain Stimulation for Parkinson Disease

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.