BACKGROUND:Previous studies show that quantitative R2* mapping can reveal iron deposition and tissue alterations, potentially aiding Parkinson's disease (PD) management. However, R2* maps are not commonly used in clinical practice due to the extra time required and sensitivity to susceptibility artifacts. PURPOSE:In this work, we propose to evaluate the feasibility of using generative-adversarial-networks (GANs) for synthesis of R2* maps from T1-weighted (T1W) and T2-weighted (T2W) images. METHODS:A GAN model was developed to synthesize R2* maps from T1W and T2W images. 572 internal participants and 268 external participants were included. The internal-dataset was divided into training (344), validation (114), and test data (114), while the external-dataset was an independent test-set. The performance of the proposed model was compared with a 2D Unet model without adversarial loss. The performance of the two models was evaluated using normalized mean square error, peak signal to noise ratio, the structure similarity index measure (SSIM), feature similarity index (FSIM), root mean square difference, average absolute difference, and relative error. Pearson method was used to assess the correlation between synthetic and real values. The area-under-the-receiver-operating-characteristic-curve (AUC) was calculated to evaluate the diagnostic efficacy of R2* maps in distinguishing PD from healthy controls (HC), with a focus on the substantia nigra pars compacta (SNpc). RESULTS:The proposed model performed better than the 2D Unet model. In internal test-set, high correlations were observed between synthetic and real R2* maps across various brain regions, with coefficients ranging from 0.75 to 0.87. The AUC was 0.79 and 0.80 for synthetic and real maps (p = 0.76), respectively. In external test-set, the AUC was 0.84 for synthetic R2* maps. Longitudinal analysis showed a positive correlation between ∆R2* and ∆UPDRS (Unified-Parkinson's-Disease-Rating-Scale) in SNpc (R = 0.69, p = 0.01) and substantia nigra pars reticulata (SNpr) (R = 0.64, p = 0.02) for PD group. CONCLUSION:The synthetic R2* maps demonstrated good correlation with real maps and performed well in diagnosing and evaluating PD in both internal and external datasets, indicating their potential value for PD diagnosis and assessment.

BACKGROUND:Deep brain stimulation (DBS) has been increasingly introduced for patients with Parkinson's disease (PD). However, there has been extensive controversy regarding its surgical timing. This study aimed to evaluate surgical outcomes of DBS across different PD durations and identify key prognostic factors. METHODS AND FINDINGS:In this multicenter cohort study, patients with PD who underwent subthalamic DBS between 1/1/2011 and 12/31/2020 from seven representative Chinese national centers were included. Two-year follow-up data were analyzed, accordingly. These patients were classified into short (<5 years), mid (5-10 years), and long (≥10 years) PD duration groups. Primary assessments included part III of the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS-III) at the off-medicine state, Hamilton Anxiety Rating Scale (HAM-A), Hamilton Depression Rating Scale (HAM-D), and Parkinson Disease Questionnaire-39 (PDQ-39) scales. Relative changes in scores were analyzed for within- and between-group comparisons, and prognostic factors were identified via multivariable linear regression. A total of 1,859 patients were screened, and 1,717 patients (749 females) were included for analysis. Respectively, 141, 978, and 598 patients underwent surgeries after short-, mid-, and long-duration. The scores of the MDS-UPDRS-III (off-medicine), HAM-A, HAM-D, and PDQ-39 significantly improved by 46.7% ± 14.1% (mean difference [MD] 25.1, 95% confidence interval [CI] [24.5, 25.7], P < 0.001), 54.4% ± 22.4% (MD 8.0, 95%CI [7.5, 8.5], P < 0.001), 43.4% ± 22.6% (MD 6.3, 95%CI [5.8, 6.8], P < 0.001), and 47.9% ± 17.8% (MD 28.0, 95%CI [27.0, 29.0], P < 0.001), respectively, and all the study groups achieved significant improvements (all P < 0.001). Notably, patients with mid-PD duration achieved greatest improvements in motor outcomes (versus short: MD 8.0%, 95%CI [4.7%, 11.3%], P = 0.008; versus long: MD 5.6%, 95%CI [2.8%, 9.4%], P = 0.01), neuropsychological evaluations (anxiety, versus long: MD 15.2%, 95%CI [12.3%, 18.1%], P = 0.002; depression, versus long: MD 19.1%, 95%CI [15.6%, 22.6%], P < 0.001), and quality of life (versus long: MD 7.6%, 95%CI [5.2%, 10.0%], P = 0.007). Levodopa response (short: adjusted β 0.42, 95% CI [0.30, 0.54], P < 0.001; mid: adjusted β 0.17, 95% CI [0.12, 0.22], P < 0.001; long: adjusted β 0.20, 95% CI [0.12, 0.28], P < 0.001) was a unified positive factor of motor response for all three groups. Higher MDS-UPDRS-III (off-medicine) scores (mid: adjusted β 0.10, 95% CI [0.05, 0.15], P < 0.001; long: adjusted β 0.30, 95% CI [0.23, 0.38], P < 0.001) were positively correlated with motor outcomes for the mid- and long-duration groups. Nevertheless, it was a negative factor for the short duration group (adjusted β -0.25, 95% CI [-0.36, -0.14], P < 0.001). The main limitation of this study is the nonrandomized observational nature introduced potential selection bias and imbalanced comparisons. CONCLUSIONS:DBS significantly improved motor, neuropsychological, and quality-of-life outcomes across all PD durations, with the most substantial benefits observed in mid-duration (5-10 years) patients. While levodopa response was a consistent positive prognostic factor for motor response, caution is warranted for short-duration patients with rapidly progressive motor symptoms, as they exhibited less favorable outcomes.

OBJECTIVES:Conventional deep brain stimulation (cDBS) is an established treatment for Parkinson's disease (PD), whereas adaptive DBS (aDBS) represents a promising approach with potential advantages in minimizing stimulation-induced side effects and enhancing quality of life. This study evaluated the safety and efficacy of a newly developed aDBS closed-loop neurostimulation (CNS) device for one year across multiple centers, with the primary objective of comparing the outcomes of aDBS and cDBS. MATERIALS AND METHODS:This retrospective study included 62 patients with PD who underwent bilateral subthalamic nucleus (STN) DBS. Outcomes were assessed using the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), Parkinson's Disease Questionnaire-39 (PDQ-39), and Schwab and England Activities Scale, whereas the levodopa-equivalent daily dose (LEDD) and adverse events were monitored. This two-phase trial randomized participants into Stim-on or Stim-off groups for 90-day postoperative comparison followed by nonrandomized evaluation of aDBS vs cDBS at 360 days after surgery. RESULTS:At 90 days postoperatively, the Stim-on group exhibited superior outcomes to those in the Stim-off group except for LEDD and speech in the medication-on state. At the 360-day postoperative assessment, the aDBS group showed significantly greater improvements than did the cDBS group in MDS-UPDRS II (57.29% vs 33.02%, p = 0.022), MDS-UPDRS IV (59.83% vs 36.69%, p = 0.026), PDQ-39 (56.91% vs 27.37%, p = 0.031), and LEDD reduction (53.35% vs 29.16%, p = 0.002). CNS aDBS recorded clear STN-beta signals, which could be adopted as a biomarker. CONCLUSIONS:Both aDBS and cDBS significantly alleviate motor symptoms and enhance quality of life in patients with PD. Although comparable in motor symptom control, aDBS indicated advantages over cDBS across LEDD reduction, MDS-UPDRS II, MDS-UPDRS IV, and PDQ-39 over the long term. Further studies with extended follow-up and larger sample sizes are required to validate these results.

X-ray machines are vital in medical imaging for viewing internal body structures. However, in lumbar vertebrae surgery, the X-ray machine has to move and position frequently and manually, which risks infection, misalignment, and overexposure to radiation. There’s a need for mobile X-ray machines with autonomous recognition and positioning. Although visual servoing suitable for X-ray positioning naturally, it still experiencing challenges like limited Field-of-View (FOV) and the structural similarity among vertebrae. In this paper, a Bayesian Estimation based approach is developed to improve the detection accuracy in case of vertebrae positions outside the FOV. Furthermore, a visual servoing method is proposed for X-ray positioning, considering their mechanical structure and imaging characteristics. The experiment results indicates that the proposed approach improves lumbar vertebrae detection accuracy significantly. This approach facilitates precise positioning in X-ray imaging and enhances the safety and effectiveness of lumbar vertebrae surgery.

Parkinson’s Disease (PD) is a growing burden with varied clinical manifestations and responses to Subthalamic Nucleus Deep Brain Stimulation (STN-DBS). At present, there is no effective and simple machine learning model based on comprehensive clinical scales to predict the improvement in motor symptoms of PD treated with DBS. A total of 647 PD patients from the First Affiliated Hospital of University of Science and Technology of China were enrolled retrospectively. LightGBM machine learning algorithm was used for modeling, and 123 PD patients from Qingdao Municipal Hospital were used as external data to verify the effectiveness of the model. The study was registered in the Chinese Clinical Trial Registry with the registration number of ChiCTR2300073955. The LightGBM model outperformed others, demonstrating an internal test set AUC of 0.874 (95

Accurately detecting spine vertebrae plays a crucial role in successful orthopedic surgery. However, identifying and classifying lumbar vertebrae from arbitrary spine X-ray images remains challenging due to their similar appearance and varying sizes among individuals. In this paper, we propose a novel approach to enhance vertebrae detection accuracy by leveraging both global and local spatial relationships between neighboring vertebrae. Our method incorporates a two-stage detector architecture that captures global contextual information using an intermediate heatmap from the first stage. Additionally, we introduce a detection head in the second stage to capture local spatial information, enabling each vertebra to learn neighboring spatial details, visibility, and relative offset. During inference, we employ a fusion strategy that combines spatial offsets of neighboring vertebrae and heatmap from a conventional detection head. This enables the model to better understand relationships and dependencies between neighboring vertebrae. Furthermore, we introduce a new representation of object centers that emphasizes critical regions and strengthens the spatial priors of human spine vertebrae, resulting in an improved detection accuracy. We evaluate our method using two lumbar spine image datasets and achieve promising detection performance. Compared to the baseline, our algorithm achieves a significant improvement of 13.6% AP in the CM dataset and surpasses 6.5% and 4.8% AP in the anterior and lateral views of the BUU dataset, respectively.

Objective: To analyze the complications related to deep brain stimulation(DBS) surgery in Parkinson's disease(PD) patients and to determine whether there is a learning curve effect in terms of complications. Methods: Retrospective analysis of the DBS surgical data of 822 PD patients performed by the same surgeon at the First Affiliated Hospital of the University of Science and Technology of China (Anhui Provincial Hospital) from December 2012 to December 2022. The complications related to DBS were evaluated and analyzed the complications of every 100 DBS surgery were further analyzed. Results: A total of 822 PD patients, 453 males and 369 females, aged 31-80 years old, were included. The minimum follow-up period after DBS surgery is 6 months. Surgical related complications occurred in 55 patients (6.69%), including 5 patients (0.61%) with slight bleeding around the electrode, 1 patient (0.12%) with cerebral infarction, 4 patients (0.49%) with postoperative epilepsy, 42 patients (5.11%) with postoperative delirium, 2 patients (0.24%) with respiratory distress, and 1 patient (0.12%) with acute cardiac insufficiency. There were 16 cases (1.94%) of hardware related complications in DBS, of which 4 cases (0.48%) had infection, 1 case (0.12%) had a broken angle at the connection between the pulse generator and the extension wire, 8 cases (0.97%) had an excessively tight extension wire, and 3 cases (0.36%) had an IPG bag hematoma. In the infected cases, 2 patients removed IPG and extension wires. There were 7 cases (0.85%) of stimulus related complications, including 4 cases (0.61%) with programmed sensory abnormalities, 1 case (0.12%) with postoperative abnormal movements and dance like movements, and 2 cases (0.24%) with psychiatric symptoms. A comprehensive analysis was conducted on the above complications, among which 8 cases (0.97%) were relatively serious complications. After active treatment, satisfactory results were achieved, and none of them affected the patient's DBS treatment effect and no patients died. For every 100 cases of DBS surgery complications were analyzed, the percentage of complications decreased significantly from 14.50% (58 cases) in the first 400 cases to 4.73% (20 cases) in the last 400 cases (P<0.001). Conclusion: DBS surgery is safe and has an acceptable low incidence of complications. The incidence of complications also decreases with the accumulation of experience, showing a learning curve effect.

Objective The aim of this study was to investigate the differences in clinical response and imaging data before and after surgery in patients with Parkinson’s disease and to attempt to predict clinical response using connectivity differences.Methods Connectivity analysis was performed on resting-state functional magnetic resonance imaging（rs-MRI） of 41 patients with Parkinson’s disease who underwent deep brain stimulation, and the subcortical connectivity network was calculated, evaluated before and one year after surgery on the UPDRSIII scale, correlated with the above-mentioned brain connectivity network, and predicted clinical motor symptoms, and predictive performance was discussed based on actual clinical improvement.Results Functional connectivity（FC） between the subcortical nuclei of interest and the DBS response showed that FC from bilateral red nuclei to the shell nuclei correlated with overall changes in UPDRS-III（left: r=-0.44P=0.0056,right r=-0.46p=0.0029,P＜0.05 after FDR correction）.In addition, FC predicted the change in UPDRSIII 1 year after DBS（r=0.5P=0.0011 error rate=0.175）.Conclusion The functional connectivity between the putamen and the red nucleus can serve as an important predictor of treatment outcomes in PD patients undergoing DBS surgery.

Objective: To analyze the imaging changes of in the early period after subthalamic nucleus (STN) deep brain stimulation (DBS) surgery for Parkinson's disease (PD) and its impact on electrode impedance by the application of 3.0T MRI-compatible devices. Methods: A retrospective analysis was performed for the data of 43 PD patients who underwent 3.0T MRI-compatible STN-DBS surgery from October 2022 to April 2023 at the First Affiliated Hospital of USTC(Anhui Provincial Hospital), including 27 males and 16 females, aged 43-68 (56±5) years. All patients underwent postoperative 3.0T MRI, CT scans,and impedance measurements 1 week postoperatively.Fifteen patients underwent 3.0T MRI and impedance measurements 1 month postoperatively. The differences in impedance of electrode contacts before and after the 3.0T MRI scans were compared. The occurrence of peri-lead cerebral edema (PLE) in patients was analyzed, as well as the differences in PLE detection rates between the two imaging methods, and the differences in the incidence and volume of PLE at different microelectrode recordings, the occurrence and detection of postoperative PLE, and different microelectrode recording (MER) times and different time nodes were compared. The correlation between electrode impedance and the volume of edema around the nucleus was analyzed. Results: All 43 patients successfully underwent surgery, with a total of 86 electrodes implanted. There was no significant difference in electrode impedance values before and after the 3.0T MRI examinations at 1 week and 1 month postoperatively. The PLE detection rate with 3.0T MRI was 95.12%(39/43), which is significantly higher than that of CT imaging 17.07% (7/43)(χ2=50.705, P<0.001). One week after surgery, the incidence and volume of PLE were higher in the multiple MER group compared with the single MER group, but the difference was not statistically significant. The volume of PLE [M(Q1, Q3) 0 (0, 1.211) cm3] at 1 month was significantly smaller than that at 1 week [0.243 (0, 2.914) cm3] (Z=-3.408, P=0.001). The impedance of electrode contacts within 1 month postoperatively showed a trend of initial decrease followed by an increase, which was negatively correlated with SE volume(r=-0.317, P=0.014). Conclusions: The application of 3.0T MRI-compatible DBS devices in the surgical treatment of PD patients improves the accuracy of early postoperative imaging assessment. The electrode impedance is more stable as the edema around the nucleus subsided at 1 month after surgery, which is suitable for the first program control.

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.

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.

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.

Heart segmentation plays an important role in accurate diagnosis and treatment of cardiovascular disease. More recently, deep convolutional neural networks (CNN) are predominant to many medical image analysis applications including 3D heart segmentation. For example, 3D UNet with U-shape encoder-decoder architecture performs well in volumetric segmentation. However, standard convolution, the building block of 3D CNN network usually contains a large number of parameters. In this paper, our objective is to investigate a light convolution module to build an efficient network. Inspired by 2D Tied Block Convolution (TBC), we introduce a Tied Block 3D Convolution (TBC-3D) operator which reuses a small number of convolution filters across each channel group. To this end, TBC-3D requires fewer parameters and is able to obtain more feature maps with high performance. Furthermore, we combine TBC-3D with the Ghost-3D module to construct Ghost Tied Block (GTB). Specifically, Ghost module employs standard convolution (OP1) with few filters to obtain intrinsic feature maps, and then generates more features by cheap linear operation like depth-wise convolution (OP2). TBC-3D is applied in both OP1 and OP2 in the Ghost-3D module. Compared to state-of-the-art solutions using 3D UNet-like architecture, our model with GTB achieves competitive performance on the MM-WHS whole heart segmentation Challenge 2017 datasets with 2.31x less parameters and 1.93x fewer FLOPs.

Abstract Background:While deep brain stimulation (DBS) of subthalamic nucleus (STN) is proved effective in managing motor symptoms of Parkinson's disease, it has substantial individulized variability of clinlcal responses. Prediction of treatment outcomes is therefore beneficial for surgical planning. This study aims to examine the capability of preoperative resting state brain connectivity as a potential tool to predict the clinlcal response of STN-DBS. Method: We collected the preoperative resting state functional Magnetic Resonance Imaging (MRI) of 41 participants who received DBS in the STN. The subcortical connectivity networks were estimated and correlated with postoperative exercise results. Linear regression was further used to predict the surgical improvements. Results: Functional Connectivity (FC) between subcortical nuclei of interest and DBS response showed that FC from bilateral red nuclei to putamen was related to the overall changes of UPDRS-III (left: r = -0.44 p = 0.0056, right r = -0.46 p = 0.0029, p < 0.05 after FDR correction). Additionally, FC can predict the changes of UPDRS III at 1 year after DBS (r = 0.5 p = 0.0011 error rate = 0.175 ). Cloclusions: The increased connectivity of the red nuclei in patients with Parkinson's disease may be a compensatory response to central nervous system damage. Functional MRI studies support the long-standing view that relatively intact cerebellar circuits can compensate for impaired basal ganglia function. Neuroplasticity is an adaptive mechanism that compensates for loss of function or maximizes residual function, leading to changes in brain function and morphology. This compensatory brain plasticity may be the reason why DBS improves the motor symptoms of Parkinson's disease.

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.

Maximum intensity projection (MIP) is a standard volume-rendering technique for 3D volumetric data processing. For example, given a 3D CT data, it simply projects the voxel values with its maximum intensity on a specific view to output a 2D image. Recently, MIP is further combined with Btrfly Net for vertebrae labelling task. However, this simple reformations of 3D data leads to loss of rich context information in volumetric data. In this paper, we propose a learned orthographic pooling approach instead of image processing based MIP. Typically, a simple conv-simple and bottleneck pooling modules are introduced to learn the orthographic projection of 3D data and output 2D intermediate feature maps. To this end, the learned orthographic pooling helps preserve detail information of 3D context during projection. Furthermore, an unified Btrfly Net is provided for vertebrae labelling by integrating the orthographic pooling sub-network. The novel Btrfly Net with orthographic pooling sub-network is evaluated on the 2014 MICCAI vertebra localization challenge dataset. Compared to original Butfly Net with MIP, orthographic pooling, the learned MIP largely boosts the performance of vertebrae labelling.

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.

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.

Background: Lack of intuitiveness and poor hand-eye coordination present a major technical challenge in neurosurgical navigation. Methods: We developed an integrated dexterous stereotactic co-axial projection imaging (sCPI) system featuring orthotopic image projection for augmented reality (AR) neurosurgical navigation. The performance characteristics of the sCPI system, including projection resolution and navigation accuracy, were quantitatively verified. The resolution of the sCPI was tested with a USAF1951 resolution test chart. The stereotactic navigation accuracy of the sCPI was measured using a calibration panel with a 7×7 circle array pattern. In benchtop validation, the navigation accuracy of the sCPI and the BrainLab Kick Navigation Station was compared using a skull phantom with 8 intracranial targets. Finally, we demonstrated the potential clinical application of sCPI through a clinical trial. Results: The resolution test showed that the resolution of the sCPI was 1.3 mm. In a stereotactic navigation accuracy test, the maximum and minimum error of the sCPI was 2.9 and 0.3 mm, and the mean error was 1.5 mm. The stereotactic navigation accuracy test also showed that the navigation error of the sCPI would increase with the pitch and yaw angle, but there was no obvious difference in navigation errors caused by different yaw directions, which meant that the navigation error is unbiased across all directions. The benchtop validation showed that the average navigation errors for the sCPI system and the Kick Navigation Station were 1.4±0.8 and 1.8±0.7 mm, the medians were 1.3 and 1.9 mm, and the average preparation times were 3 min 24 sec and 6 min 8 sec, respectively. The clinical feasibility of sCPI-assisted neurosurgical navigation was demonstrated in a clinical study. In comparison with the BrainLab device, the sCPI system required less time for preoperative preparation and enhanced the clinician experience in intraoperative visualization and navigation. Conclusions: The sCPI technique can be potentially used in many surgical applications for intuitive visualization of medical information and intraoperative guidance of surgical trajectories.