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Deep brain stimulation (DBS) surgery is a well-established therapy for control of motor symptoms in Parkinson’s disease. Despite an appropriate targeting and an accurate placement of DBS lead, a thorough and efficient programming is critical for a successful clinical outcome. DBS programming is a time consuming and laborious manual process. The current approach involves use of general guidelines involving determination of the lead type, electrode configuration, impedance check, and battery check. However there are no validated and well-established programming protocols. In this review, we will discuss the current practice and the recent advances in DBS programming including the use of interleaving, fractionated current, directional steering of current, and the use of novel DBS pulses. These technological improvements are focused on achieving a more efficient control of clinical symptoms with the least possible side effects. Other promising advances include the introduction of computer guided programming which will likely impact the efficiency of programming for the clinicians and the possibility of remote Internet based programming which will improve access to DBS care for the patients.

Clinical responses to dopamine replacement therapy for individuals with Parkinson’s disease (PD) are often difficult to predict. We characterized changes in MDS-UPDRS motor factor scores resulting from a short-duration L-Dopa response (SDR), and investigated how the inter-subject clinical differences could be predicted from motor cortical magnetoencephalography (MEG). MDS-UPDRS motor factor scores and resting-state MEG recordings were collected during SDR from twenty individuals with a PD diagnosis. We used a novel subject-specific strategy based on linear support vector machines to quantify motor cortical oscillatory frequency profiles that best predicted medication state. Motor cortical profiles differed substantially across individuals and showed consistency across multiple data folds. There was a linear relationship between classification accuracy and SDR of lower limb bradykinesia, although this relationship did not persist after multiple comparison correction, suggesting that combinations of spectral power features alone are insufficient to predict clinical state. Factor score analysis of therapeutic response and novel subject-specific machine learning approaches based on subject-specific neuroimaging provide tools to predict outcomes of therapies for PD.

Subthalamic nucleus (STN) or globus pallidus interna (GPi) deep brain stimulation (DBS) is considered a robust therapeutic tool in the treatment of Parkinson’s disease (PD) patients, although it has been reported to potentially cause cognitive decline in some cases. We here provide an in-depth and critical review of the current literature regarding cognition after DBS in PD, summarizing the available data on the impact of STN and GPi DBS as monotherapies and also comparative data across these two therapies on 7 cognitive domains. We provide evidence that, in appropriately screened PD patients, worsening of one or more cognitive functions is rare and subtle after DBS, without negative impact on quality of life, and that there is very little data supporting that STN DBS has a worse cognitive outcome than GPi DBS.

Twitter is a free, open access social media platform that is widely used in medicine by physicians, scientists, and patients. It provides an opportunity for advocacy, education, and collaboration. However, it is likely not utilized to its full advantage by many disciplines in medicine, and pitfalls exist in its use. In particular, there has not been a review of Twitter use and its applications in the field of neurology. This review seeks to provide an understanding of the current use of Twitter in the field of neurology to assist neurologists in engaging with this potentially powerful application to support their work.

This study aims to establish a multicenter database to evaluate Parkinson's disease in the MENASA region in the context of expert care. The CGD-PD consortium includes 20 institutes from 9 MENASA countries. The database collects comprehensive data from PD patients. Initial data from participating sites showed significant heterogeneity in patient demographics, clinical characteristics, and healthcare management within the MENASA area. Descriptive analyses will include patient demographics and treatment methods, while multilevel regression models will explore correlations across care levels, environmental factors, and health outcomes. The results are anticipated to reveal region-specific patterns and gaps in the management of Parkinson's disease. The CGD-PD database will be instrumental in addressing the gap in PD research in the MENASA region, ultimately improving the quality of life for PD patients.

Neuromodulation therapies, including deep brain stimulation (DBS) and pump therapies, are currently the standard of care for PD patients with advanced disease and motor complications that are difficult to control with medical management alone. The quest for alternate lesser invasive approaches led to the development of several novel therapies like intrajejunal levodopa infusions (IJLI), continuous subcutaneous apomorphine infusions (CSAI) and Magnetic Resonance guided Focused Ultrasound (MRgFUS) in recent years. To achieve good outcomes with any of these therapeutic modalities, careful patient selection, multidisciplinary evaluation and technical expertise are equally important. In this review, we will provide an overview of the neuromodulation strategies currently available for PD, emphasizing on patient selection and choosing among the various strategies.

IntroductionIndividuals with schizophrenia are at a high risk of physical health comorbidities and premature mortality. Cardiovascular and metabolic causes are an important contributor. There are gaps in monitoring, documenting and managing these physical health comorbidities. Because of their condition, patients themselves may not be aware of these comorbidities and may not be able to follow a lifestyle that prevents and manages the complications. In many low-income and middle-income countries including Pakistan, the bulk of the burden of care for those struggling with schizophrenia falls on the families.ObjectivesTo determine the rate of self-reported physical health disorders and risk factors, like body mass index (BMI) and smoking, associated with cardiovascular and metabolic disorders in cases of schizophrenia compared with a group of mentally healthy controls.DesignA case-controlled, cross-sectional multicentre study of patients with schizophrenia in Pakistan.SettingsMultiple data collection sites across the country for patients, that is, public and private psychiatric OPDs (out patient departments), specialised psychiatric care facilities, and psychiatric wards of teaching and district level hospitals. Healthy controls were enrolled from the community.ParticipantsWe report a total of 6838 participants’ data with (N 3411 (49.9%)) cases of schizophrenia compared with a group of healthy controls (N 3427 (50.1%)).ResultsBMI (OR 0.98 (CI 0.97 to 0.99), p=0.0025), and the rate of smoking is higher in patients with schizophrenia than in controls. Problems with vision (OR 0.13 (0.08 to 0.2), joint pain (OR 0.18 (0.07 to 0.44)) and high cholesterol (OR 0.13 (0.05 to 0.35)) have higher reported prevalence in controls. The cases describe more physical health disorders in the category ‘other’ (OR 4.65 (3.01 to 7.18)). This captures residual disorders not listed in the questionnaire.ConclusionsParticipants with schizophrenia in comparison with controls report more disorders. The access in the ‘other’ category may be a reflection of undiagnosed disorders.