Deep brain stimulation (DBS) is an established therapy for Parkinson’s disease, yet conventional onsite programming mandates frequent travel to specialized centres, imposing substantial burdens on patients. Here we present a large real-world analysis of remote programming (RP) for DBS in China, drawing on 20,383 patients with Parkinson’s disease and 42,163 RP sessions (2012–2024). RP achieves comparable satisfaction and effectiveness to onsite programming while reducing the healthcare access inequality index by 30%. These access gains translate into disproportionate economic benefits for the most vulnerable groups, with cost savings two to ten times greater among low-income, remote and advanced-disease populations. Integrated clinical–labour–economy modelling projects annual direct economic benefits of ¥1.09 billion from reduced domestic medical tourism and ¥8.15 billion from labour-cost savings, with cumulative benefits of ¥115–270 billion by 2050 (3.9–9.2% of China’s 2024 basic medical insurance fund). These findings suggest that RP could be a clinically equivalent, more equitable and economically advantageous approach for postoperative DBS management worldwide. After more than 10 years of real-world deployment involving 20,383 patients, this analysis reports on the cost-effectiveness of remote programming of deep brain stimulation, outlining the implications for reducing inequalities in healthcare access and projecting scenarios of large-scale implementation in the context of an ageing population.

Background:A simple, rapid, and effective cognitive screening test appropriate for fast-paced settings with limited resources and staff is essential, especially preoperatively. This study aimed to develop and validate the short versions of Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) for predicting postoperative delirium (POD) in patients with Parkinson's disease (PD) who were scheduled for surgery.Methods:The current study was a secondary analysis of data collected from 128 inpatients scheduled for deep brain stimulation of the subthalamic nuclei (STN-DBS) lasting >60 min, at Tsinghua University Yuquan Hospital, China. Preoperative cognitive screening was performed during the preoperative visit using the MMSE and MoCA. The optimal MMSE and MoCA cut-off scores for detecting PD-MCI was 27 and 23 respectively. The POD was assessed twice a day on the first postoperative day until discharge by the confusion assessment method. The backward conditional logistic regression analysis was used to organize the reduced versions of the MMSE or MoCA. Also, the areas under the receiver operating characteristic curves (AUCs) were examined using the DeLong test.Results:125/128 PD patients were included in the analysis, and 27 (21.6%) developed POD. The MMSE reduced version (orientation to time, attention and calculation, and comprehension) demonstrated performance similar to the original MMSE in predicting POD (z=0.820, p=0.412). The AUC of the original MoCA and the short MoCA (visuospatial and executive attention and orientation) were 0.808 and 0.826, respectively. There was no significantly difference in the AUC values between the tests (z=0.561, p=0.575).Conclusion:Our simplified MMSE and MoCA could be efficiently used to identify patients at risk for POD. Also, short cognitive tests could be considered while predicting POD in fast-paced preoperative settings with limited resources and staff.

Purpose: To predict the prognosis of craniopharyngioma in children by optical coherence tomography angiography (OCTA).Methods: We evaluated if the relationship between preoperative OCTA of the choroidal capillary density (CCD) and visual outcome continued over long-term visual recovery in 38 patients undergoing craniopharyngioma resection. Patients were evaluated 3 times: 1 week before surgery (Visit1), followed-up at 6–10 weeks (Visit2), and 9–15 months (Visit3) after surgery.Results: In total 38 patients (70 eyes) with craniopharyngiomas, which included 20 (52.6%) boys and 18 (47.4%)girls, the mean age was 11.8 ± 2.7 years (range: 6–18 years). The age (p = 0.71), gender (p = 1.00), mean refractive error (p = 0.55), and axial length (p = 0.23) of 38 normal volunteers (76 eyes) were matched. After surgery, the cross-compression of patients was relieved. The average visual acuity change in the normal CCD group was 0.07 ± 0.02; the average visual acuity change in the low CCD group was 0.01 ± 0.01, p < 0.001. Preoperative CCD value is related to the preoperative BCVA (p < 0.001), and the visual function after the long-term follow-up (9–15 months) (p < 0.001). The prognosis of CCD has the same trend as the BCVA. Further correlation analysis shows that CCD and BCVA are significantly correlated (r = 0.878; p < 0.001). CCD has a weak but significant correlation both with MD (r = 0.19; p < 0.001) and PSD (r = −0.21; p <0.001). A natural cutoff of CCD is approximately 38%. With the normal CCD group the maximum improvement of BCVA exceeds 0.3 post-operatively, compared to eyes in the low CCD group that improve by <0.03, and worse after surgery.Conclusions: Long-term vision recovery after surgical decompression of craniopharyngiomas in children can be predicted by preoperative by OCTA. Patients with normal CCD before surgery showed a tendency to improve vision; this trend of improvement persisted in subsequent follow-ups. The CCD baseline natural cutoff value for predicting visual prognosis before and after surgery is about 38%.

Objective: This study aimed to explore possible biomarkers of postoperative delirium (POD) of Parkinson’s disease (PD) patients received deep brain stimulation (DBS) of the subthalamic nuclei. Materials and methods: This nested case control study analyzed perioperative plasma and cerebral spinal fluid (CSF) of patients (n = 40) who developed POD undergone DBS surgery (n = 10) and those who did not (n = 30). Blood sample was collected before surgery and on the first day postoperative, CSF sample was collected at the beginning of the operation. POD was assessed by the Confusion Assessment Method (CAM) twice a day between 7:00 am and 7:00 pm after the surgery until discharge. Plasma and CSF sample from the two groups were analyzed to investigate possible biomarkers for POD in PD patients. Results: There was no difference between POD and Non-POD groups on the concentration of Interleukin 6 and Tumor Necrosis Factor-α in CSF, preoperative plasma and postoperative plasma. There was no difference between POD and Non-POD groups on the concentration of S100 calcium-binding protein β protein (S100β) and Neurofilament light chain (NFL) in preoperative plasma and postoperative plasma. The concentration of C-reactive protein (CRP), NFL and S100β were significant higher in POD group than non-POD group in CSF. The concentration of CRP was significantly higher in POD group than non-POD group in preoperative plasma and postoperative plasma. CSF concentration of S100β might be a potential biomarker for POD via the receiver operating characteristic curve analysis and the area under the curve value of 0.973. Conclusion: For PD patients received DBS surgery, CSF S100β might be a marker for aiding detection of high-risk patients with delirium. This requires further confirmation in clinical trials.

The authors noticed that in Fig. 6, there are similarities between the two images: WT DBS group and APP/PS1 control group in Hoechst staining. This error was inadvertently made during image assembly. The error has now been corrected as shown below. This correction does not affect the results or conclusions of the work. We apologize for any inconvenience caused. The neuroprotective effect of deep brain stimulation at nucleus basalis of Meynert in transgenic mice with Alzheimer's diseaseBrain Stimulation: Basic, Translational, and Clinical Research in NeuromodulationVol. 12Issue 1PreviewAlzheimer's disease (AD) is the most common type of dementia and mainly treated by drugs, while the therapeutic outcomes are very limited. This study aimed to determine the optimized parameters of deep brain stimulation (DBS) which was applied to the treatment of AD and propose the involved mechanisms. Full-Text PDF

Parkinson’s disease (PD) is a neurodegenerative disease that is associated with motor and non-motor symptoms and caused by lack of dopamine in the substantia nigra of the brain. Subthalamic nucleus deep brain stimulation (STN-DBS) is a widely accepted therapy of PD that mainly inserts electrodes into both sides of the brain. The effect of STN-DBS was mainly for motor function, so this study focused on the recovery of motor function for PD after DBS. Hemispherical asymmetry in the brain network is considered to be a potential indicator for diagnosing PD patients. This study investigated the value of hemispheric brain connection asymmetry in predicting the DBS surgery outcome in PD patients. Four types of brain connections, including left intra-hemispheric (LH) connection, right intra-hemispheric (RH) connection, inter-hemispheric homotopic (Ho) connection, and inter-hemispheric heterotopic (He) connection, were constructed based on the resting state functional magnetic resonance imaging (rs-fMRI) performed before the DBS surgery. We used random forest for selecting features and the Ridge model for predicting surgical outcome (i.e., improvement rate of motor function). The functional connectivity analysis showed that the brain has a right laterality: the RH networks has the best correlation (r = 0.37, p = 5.68E-03) between the predicted value and the true value among the above four connections. Moreover, the region-of-interest (ROI) analysis indicated that the medioventral occipital cortex (MVOcC)–superior temporal gyrus (STG) and thalamus (Tha)–precentral gyrus (PrG) contributed most to the outcome prediction model for DBS without medication. This result provides more support for PD patients to evaluate DBS before surgery.

Levodopa is the most‐commonly used therapy for Parkinson's Disease (PD). Imaging findings show increased cerebral blood flow (CBF) response to levodopa, but the artery morphological change is less studied.

Background: Acupuncture has become an important alternative clinical treatment for Parkinson’s disease (PD), but its efficacy and the underlying mechanisms remain debatable. Using a newly developed magnetic resonance angiography (MRA) method that has higher sensitivity for smaller and distal vessels and a novel tool that can trace vessels and extract vascular features, the immediate effects of acupuncture on intracranial vessels and blood flow in patients with PD as well as correlations with clinical outcomes were quantitatively evaluated. Methods: Fifteen PD patients received acupuncture at the Dazhui and Fengchi acupoint positions. MRA was performed before and after 30 min of treatment. The cerebral blood flow (CBF) and the length, volume, diameter, and signal intensity of the intracranial internal carotid artery (ICA) and middle cerebral artery (MCA) were measured. The Unified Parkinson’s Disease Rating Scale part III (UPDRS-III) and Visual Analogue Scale (VAS) were used to evaluate the motor symptoms and the subjective feelings of discomfort. Results: Acupuncture significantly reduced UPDRS-III and VAS scores. No significant changes were noted in the overall CBF before and after treatment. However, there was a significant extension effect on the length of the intracranial ICA and MCA and the distal MCA, and a significant increase in the number of branches of the MCA was found. Although acupuncture tended to increase the total volume of the intracranial ICA and the volume of the MCA, no statistical significance was reached. The total intensity was not altered, but the intensity and diameter of the M1 segment were significantly increased, whereas the intensity of the MCA was decreased. A positive correlation between M1 intensity changes and UPDRS-III changes was found. Conclusions: Angiographic evaluation suggested that acupuncture had a significant effect on intracranial blood vessels, which is one possible mechanism for acupuncture improving the motor symptoms of PD.

Parkinson's Disease (PD) is a common neurodegenerative disease which impacts millions of people around the world. In clinical treatments, freezing of gait (FoG) is used as the typical symptom to assess PD patients' condition. Currently, the assessment of FoG is usually performed through live observation or video analysis by doctors. Considering the aging societies, such a manual inspection based approach may cause serious burdens on the healthcare systems. In this study, we propose a pure video-based method to automatically detect the shuffling step, which is the most indistinguishable type of FoG. Firstly, the RGB silhouettes which only contain legs and feet are fed into the feature extraction module to obtain multi-level features. 3D convolutions are used to aggregate both temporal and spatial information. Then the multi-level features are aggregated by the feature fusion. Skip connections are implemented to reserve information of high resolution and period-wise horizontal pyramid pooling is utilized to fuse both global context and local features. To validate the efficacy of our method, a dataset containing 268 normal gait samples and 362 shuffling step samples is built, on which our method achieves an average detection accuracy of 90.8%. Besides shuffling step detection, we demonstrate that our method can also assess the severity of walking abnormity. Our proposal facilitates a more frequent assessment of FoG with less manpower and lower cost, leading to more accurate monitoring of the patients' condition.

Freezing of Gait (FoG) is a common symptom among patients with Parkinson’s Disease (PD). In this paper, a vision-based method is proposed to automatically recognize the shuffling step symptom from the Timed Up-and-Go (TUG) videos based. In this method, a feature extraction block is utilized to extract features from image sequences, then features are fused along a temporal dimension, and these features are fed into a classification layer. In this experiment, the dataset with 364 normal gait examples and 362 shuffling step examples is used. And the experiment on the collected dataset shows that the average accuracy of the best method is 91.3%. Using this method, the symptom of the shuffling step can be recognized automatically and efficiently from TUG videos, showing the possibility to remotely monitor the movement condition of PD patients.

Due to its importance in clinical science, the estimation of physiological states (e.g., the severity of pathological tremor) has aroused growing interest in machine learning community. While the physiological state is a continuous variable, its continuity is lost when the physiological state is quantized into a few discrete classes during recording and labeling. The discreteness introduces misalignment between the true value and its label, meaning that these labels are unfortunately imprecise and coarse-grained. Most previous work did not consider the inaccuracy and directly utilized the coarse labels to train the machine learning algorithms, whose predictions are also coarse-grained. In this work, we propose to learn a precise, fine-grained estimation of physiological states using these coarse-grained ground truths. Established on mathematical rigorous proof, we utilize imprecise labels to restore the probabilistic distribution of precise labels in an approximate order-preserving fashion, then the deep neural network learns from this distribution and offers fine-grained estimation. We demonstrate the effectiveness of our approach in assessing the pathological tremor in Parkinson's Disease and estimating the systolic blood pressure from bioelectrical signals.

BACKGROUND: Deep brain stimulation (DBS) is an important treatment for patients with advanced Parkinson's disease (PD). Patients after DBS implantation need specialized programming to get optimal outcomes. However, access to timely and economical postoperative programming for many patients living in remote areas is limited. Teleprogramming, which refers to deliver real-time remote programming through Internet, can help to address this gap. OBJECTIVE: We aimed to evaluate the clinical application of remote programming for PD patients with DBS. METHODS: We retrospectively studied 90 patients with PD who received remote DBS programming after implantation at Yuquan Hospital (Beijing, China) between March 2016 and June 2018. Patients' medical records were reviewed in an electronic database. A self-designed questionnaire was performed on all patients by phone. RESULTS: Over a mean follow-up period of 27.0 months, 90 patients underwent a total of 386 remote programming visits, of which the average frequency within 6 months after DBS was 2.27 times/person. The average distance between the patients' residences and Yuquan Hospital was 1243.8 +/- 746.5 km. The questionnaire survey showed that each remote programming visit saved >= 2000(sic) for 76.7% of the patients and >= 12 hours for 90.0% of the patients, compared with the on-site programming visit. The acceptability of the remote programming platform was highly rated. Transient side effects related to programming were reported and were relieved after adjustments of parameters. CONCLUSIONS: Remote programming may offer a feasible and acceptable approach to timely and economic management in patients with PD after DBS implantation.

Parkinson’s disease（PD） is a progressive neurodegenerative disorder commonly observed in middle-aged and elderly. Currently, its etiology and pathogenesis are still not completely understood. It is associated with many symptoms that severely affect patients’ health and quality of life. At present, the PD clinical treatment mainly aimed to alleviate symptoms, and both medicinal and surgical treatments have side effects and treatment blind spots. The use of acupuncture for the treatment of PD is relatively widespread, and its safety and efficacy have been gradually accepted by the public and medical professions. However, the efficacy of acupuncture in experimental studies remains controversial. Therefore, this paper reviews imaging studies on the use of acupuncture for the treatment of PD. From the study, it shows that acupuncture can improve the neuronal activity, activate the neuronal activity in damaged brain regions, affect relevant neural networks and brain circulation,improve cerebral metabolism, and cause structural changes in related brain regions. Intuitive and visible imaging studies provide objective bases on the use of acupuncture for the treatment of PD.

Human parsing has recently attracted considerable interests due to its wide application potentials. However, developing an accurate human parsing system is still a challenge for researchers. In this paper, we demonstrate that global information are critical for accurate prediction by applying a non-local operation for effectively extracting global information. Meanwhile several training data refinement methodologies are proposed to further boost the performance. Benefiting from all the approaches, the proposed single human parsing model NLGINet achieves the state-of-the-art segmentation accuracy on two human parsing benchmark datasets LIP and Pascal-Person-Parts.

Parkinson's disease (PD) is a multi-systemic disease in the brain arising from the dysfunction of several neural networks. The diagnosis and treatment of PD have gained more attention for clinical researchers. While there have been many fMRI studies about functional topological changes of PD patients, whether the dynamic changes of functional connectivity can predict the drug therapy effect is still unclear. The primary objective of this study was to assess whether large-scale functional efficiency changes of topological network are detectable in PD patients, and to explore whether the severity level (UPDRS-III) after drug treatment can be predicted by the pre-treatment resting-state fMRI (rs-fMRI). Here, we recruited 62 Parkinson's disease patients and calculated the dynamic nodal efficiency networks based on rs-fMRI. With connectome-based predictive models using the least absolute shrinkage and selection operator, we demonstrated that the dynamic nodal efficiency properties predict drug therapy effect well. The contributed regions for the prediction include hippocampus, post-central gyrus, cingulate gyrus, and orbital gyrus. Specifically, the connections between hippocampus and cingulate gyrus, hippocampus and insular gyrus, insular gyrus, and orbital gyrus are positively related to the recovery (post-therapy severity level) after drug therapy. The analysis of these connection features may provide important information for clinical treatment of PD patients.

Objective To study the outcomes of the revision surgeries correcting electrode malposition in deep brain stimulation (DBS) for Parkinson's disease (PD).Methods A total of 18 PD patients were enrolled who underwent lead revision surgeries due to subthalamic nucleus (STN) DBS lead malposition at Neuromodulation Center,Department of Neurosurgery,Tsinghua University Yuquan Hospital between September 2013 and May 2018.Changes in UPDRS-Ⅲ scores on and off medication were analyzed at 1 month and 6 months postoperatively compared to those before surgery.MR images were also collected to analyze the lead location change.Results Among the 18 patients,14 had bilateral lead revision and 4 had unilateral revision.A total of 32 leads were revised,among which 25 were reimplanted and the other 7 were repositioned without reimplantation.Off medication,the average UPDRS-Ⅲ scores before and 1 month and 6 months after surgery were 44.0 ± 10.5,22.4 ± 7.4 and 25.3 ± 7.7 respectively.On medication,those were 27.3 ± 9.1,16.7 ± 8.7 and 20.3 ± 8.3 respectively.The analysis of variance and comparisons between groups showed that the on and off medication scores were significantly reduced after the revision surgery (P ＜ 0.05).The off medication score at 6 months was averagely improved by (40.7 ± 4.7) ％ (5.9-79.5％).The average distance between pre-implantation and post-implantation lead locations on the plane 3.5 mm below AC (anterior commissure)-PC (posterior commissure) was (2.8 ± 1.0) mm (1.2-4.5 mm).No adverse events such as hemorrhage or infection were observed after surgery.Conclusion Lead revision surgery could safely and effectively remedy the suboptimal outcomes due to electrode malposition in STN-DBS for PD patients.

Purpose: Simultaneous noncontrast angiography and intraplaque (SNAP) imaging, as a noncontrast-enhanced MRA technique, may not provide consistent vessel visualization for intracranial artery imaging among subjects. This study aims to investigate the underlying mechanism and extend SNAP to dynamic MRA. Methods: The cause of the instability of intracranial SNAP-MRA was investigated through theoretical analysis and simulations. The scan parameters, including TI and flip angle, were optimized for reliable imaging. In vivo experiments were conducted to validate the simulation results. The simulation results were correlated with real intracranial blood flow by introducing the concept of blood travel time, and intracranial SNAP-MRA was revealed to reflect the cerebral blood expanse region in 5 TI. A new noncontrast-enhanced dynamic MRA technique, termed 4D SNAP-MRA, was proposed and demonstrated through in vivo scans. Results: The cause of the instability of intracranial SNAP-MRA was proved to be the slow or fast blood flow in the imaging slab. This instability can be mitigated by adjusting TI and flip angle in the SNAP sequence. The proposed 4D SNAP-MRA can provide dynamic visualization of the cerebral blood circulation and cerebral hemodynamic information such as blood travel time. Conclusion: The 3D SNAP-MRA with optimal imaging parameters can generate cerebral angiography with hemodynamic information, and the 4D SNAP-MRA provides dynamic visualization of the cerebral blood circulation.

We propose a deep learning based approach for quantifying the tremor severity of Parkinson's disease (PD) based on surface electromyography (sEMG). We design the S-Net, a light weight and computational efficient convolutional neural network that learns the similarity between sEMG signals in terms of the tremor severity. Labeled sEMG samples are used for jointly voting for the final results. Experiments on 147 PD patients demonstrate that our approach outperforms traditional methods by a significant margin. In addition, our approach is simple and has potentials in real applications.

Non-volitional discontinuation of motion, namely bradykinesia, is a common motor symptom among patients with Parkinson’s disease (PD). Evaluating bradykinesia severity is an important part of clinical examinations on PD patients in both diagnosis and monitoring phases. However, subjective evaluations from different clinicians often show low consistency. The research works that explore objective quantification of bradykinesia are mostly based on highly-integrated sensors. Although these sensor-based methods demonstrate applaudable performance, it is unrealistic to promote them for wide use because the special devices they require are far from popularized in daily lives. In this paper, we take advantage of computer vision and machine learning technologies, proposing a vision-based method to automatically and objectively quantify bradykinesia severity. Three bradykinesia-related items are investigated in our study: finger tapping, hand clasping and hand pro/supination. In our method, human pose estimation technology is utilized to extract kinematic characteristics and supervised-learning-based classifiers are employed to generate score ratings. Clinical experiment on 60 patients shows that the scoring accuracy of our method over 360 examination videos is 89.7%, which is competitive with other related works. The devices our method requires are only a camera for instrumentation and a laptop for data processing. Therefore, our method can produce reliable assessment results on Parkinsonian bradykinesia with minimal device requirement, showing great potential of realizing long-term remote monitoring on patients’ condition.

BACKGROUND: Skin complications are a frequent type of complication of deep brain stimulation (DBS) neurosurgical procedure and are always observed in the postauricular area, scalp area, and implantable pulse generator pocket. Modifications to the surgical techniques for DBS have been proposed as therapeutic options. To prevent skin complications, we modified the surgical incisions. METHODS: At our center, we retrospectively analyzed the complications associated with traditional surgical incisions and then adjusted the double C-shaped incision and changed the postauricular incision from vertical to horizontal in patients undergoing DBS for movement disorders. RESULTS: Skin complications were observed in 4 patients among 30 patients who underwent traditional surgical incisions. In 102 consecutive patients who underwent operations with modified surgical incisions, we did not encounter any skin complications such as skin infection or erosion related to our modified incision. A single patient experienced pain and was cured after a week of local antiinflammatory treatment. CONCLUSION: By trying to avoid placing hardware directly under the suture line, our modified surgical incision successfully reduces the incidence of skin complications.