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Gait analysis is crucial for the detection and management of various neurological and musculoskeletal disorders. The identification of gait events is valuable for enhancing gait analysis, developing accurate monitoring systems, and evaluating treatments for pathological gait. The aim of this work is to introduce the Smart-Insole Dataset to be used for the development and evaluation of computational methods focusing on gait analysis. Towards this objective, temporal and spatial characteristics of gait have been estimated as the first insight of pathology. The Smart-Insole dataset includes data derived from pressure sensor insoles, while 29 participants (healthy adults, elderly, Parkinson’s disease patients) performed two different sets of tests: The Walk Straight and Turn test, and a modified version of the Timed Up and Go test. A neurologist specialized in movement disorders evaluated the performance of the participants by rating four items of the MDS-Unified Parkinson’s Disease Rating Scale. The annotation of the dataset was performed by a team of experienced computer scientists, manually and using a gait event detection algorithm. The results evidence the discrimination between the different groups, and the verification of established assumptions regarding gait characteristics of the elderly and patients suffering from Parkinson’s disease.

Background Rituximab, a B-cell depleting monoclonal antibody, represents an option for the treatment of refractory myasthenia gravis (MG). Its use is more established in muscle-specific tyrosine kinase positive (MuSK +) patients, while its role in managing acetylcholine receptor positive (AChR +), or double seronegative (DSN) patients, remains less clear. This study evaluates the long-term effectiveness and safety of rituximab in MG of various serotypes. Methods We conducted an open-label study of MG patients receiving rituximab. Adults with generalized refractory MG, either anti-AChR + or DSN, and anti-MuSK + , refractory or not, who had follow-up > 12 months were selected. Change in quantitative myasthenia gravis (QMG) score at last follow-up, compared with baseline was a primary outcome, as well as factors affecting response to treatment. Secondary outcomes included, long-term safety, the steroid-sparing effect and relapse rates post-rituximab. Results Thirty patients (16 anti-AChR + , 6 anti-MuSK + , 8 DSN) followed for a mean of 33.3 months were included. Mean scores pre-rituximab compared to last follow-up significantly decreased (p < 0.001), from 11 ± 4.1 to 4.3 ± 3.8, and from 1.9 to 0.3 regarding QMG and relapse rate per patient/year, respectively, while in 93.1% a daily steroid dose ≤ 10 mg was achieved. Antibody status was the only factor independently influencing several endpoints. Throughout the study period no crises or deaths occurred. Conclusion The present study supports that rituximab is an effective and well tolerated treatment for refractory anti-AChR + and DSN MG patients, while anti-MuSK + remains the group experiencing the greater benefits.

Background Subthalamic nucleus (STN) deep brain stimulation (DBS) represents a well-established treatment for patients with advanced Parkinson's disease (PD) insufficiently controlled with medical therapies. This study presents the long-term outcomes of patients with PD treated with STN-DBS using an MRI-guided/MRI-verified approach without microelectrode recording. Methods A cohort of 41 patients who underwent STN-DBS were followed for a minimum period of 5 years, with a subgroup of 12 patients being followed for 8–11 years. Motor status was evaluated using part III of the Unified Parkinson's Disease Rating Scale (UPDRS-III), in on- and off-medication/on-stimulation conditions. Preoperative and postoperative assessments further included activities of daily living (UPDRS-II), motor complications (UPDRS-IV), neuropsychological and speech assessments, as well as evaluation of quality of life. Active contacts localisation was calculated and compared with clinical outcomes. Results STN-DBS significantly improved the off-medication UPDRS-III scores, compared with baseline. However, UPDRS scores increased over time after DBS. Dyskinesias, motor fluctuations and demands in dopaminergic medication remained significantly reduced in the long term. Conversely, UPDRS-III on-medication scores deteriorated at 5 and 8 years, mostly driven by axial and bradykinesia subscores. Quality of life, as well as depression and anxiety scores, did not significantly change at long-term follow-up compared with baseline. In our series, severe cognitive decline was observed in 17.1% and 16.7% of the patients at 5 and 8 years respectively. Conclusions Our data confirm that STN-DBS, using an MRI-guided/MRI-verified technique, remains an effective treatment for motor ‘off’ symptoms of PD in the long term with low morbidity.

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and nonmotor impairment with various implications on patients’ quality of life. Since currently available therapies are only symptomatic, identifying individuals with prodromal, preclinical, or early-stage PD is crucial, as they would be ideal candidates for future disease-modifying therapies. Our analysis aims to develop a robust model for accurate PD detection using accelerometer data collected from PD and non-PD individuals with mild or no tremor during phone conversations. An open-access dataset comprising accelerometer recordings from 22 PD patients and 11 healthy controls (HCs) was utilized. The data were preprocessed to extract relevant time-, frequency-, and energy-related features, and a bidirectional long short-term memory (Bi-LSTM) model with attention mechanism was employed for classification. The performance of the model was evaluated using fivefold cross-validation, and metrics of accuracy, precision, recall, specificity, and f1-score were computed. The proposed model demonstrated high accuracy (98%), precision (99%), recall (98%), specificity (96%), and f1-score (98%) in accurately distinguishing PD patients from HCs. Our findings indicate that the proposed model outperforms existing approaches and holds promise for detection of PD with subtle symptoms, like tremor, in the wild. Such symptoms can present in the early or even prodromal stage of the disease, and appropriate mobile-based applications may be a practical tool in real-life settings to alert individuals at risk to seek medical assistance or give patients feedback in monitoring their symptoms.

Parkinson’s disease (PD) is characterized by a variety of motor and non-motor symptoms, some of them pertaining to gait and balance. The use of sensors for the monitoring of patients’ mobility and the extraction of gait parameters, has emerged as an objective method for assessing the efficacy of their treatment and the progression of the disease. To that end, two popular solutions are pressure insoles and body-worn IMU-based devices, which have been used for precise, continuous, remote, and passive gait assessment. In this work, insole and IMU-based solutions were evaluated for assessing gait impairment, and were subsequently compared, producing evidence to support the use of instrumentation in everyday clinical practice. The evaluation was conducted using two datasets, generated during a clinical study, in which patients with PD wore, simultaneously, a pair of instrumented insoles and a set of wearable IMU-based devices. The data from the study were used to extract and compare gait features, independently, from the two aforementioned systems. Subsequently, subsets comprised of the extracted features, were used by machine learning algorithms for gait impairment assessment. The results indicated that insole gait kinematic features were highly correlated with those extracted from IMU-based devices. Moreover, both had the capacity to train accurate machine learning models for the detection of PD gait impairment.

Deep brain stimulation (DBS) has been proposed as a treatment option for severe Tourette's syndrome on the basis of findings from open-label series and small double-blind trials. We aimed to further assess the safety and efficacy of bilateral globus pallidus internus (GPi) DBS in patient's with severe Tourette's syndrome. In a randomised, double-blind, crossover trial, we recruited eligible patients (severe medically refractory Tourette's syndrome, age ≥20 years) from two clinics for tertiary movement disorders in the UK. Enrolled patients received surgery for GPi DBS and then were randomly assigned in a 1:1 ratio (computer-generated pairwise randomisation according to order of enrolment) to receive either stimulation on-first or stimulation off-first for 3 months, followed by a switch to the opposite condition for a further 3 month period. Patients and rating clinicians were masked to treatment allocation; an unmasked clinician was responsible for programming the stimulation. The primary endpoint was difference in Yale Global Tic Severity Scale (YGTSS) total score between the two blinded conditions, assessed with repeated measures ANOVA, in all patients who completed assessments during both blinded periods. After the end of the blinded crossover phase, all patients were offered continued DBS and continued to have open-label stimulation adjustments and objective assessments of tic severity until database lock 1 month after the final patient's final trial-related visit. This trial is registered with ClinicalTrials.gov, number NCT01647269. Between Nov 5, 2009, and Oct 16, 2013, we enrolled 15 patients (11 men, four women; mean age 34·7 years [SD 10·0]). 14 patients were randomly assigned and 13 completed assessments in both blinded periods (seven in the on-first group, six in the off-first group). Mean YGTSS total score in these 13 patients was 87·9 (SD 9·2) at baseline, 80·7 (SD 12·0) for the off-stimulation period, and 68·3 (SD 18·6) for the on-stimulation period. Pairwise comparisons in YGTSS total scores after Bonferroni correction were significantly lower at the end of the on-stimulation period compared with the off-stimulation period, with a mean improvement of 12·4 points (95% CI 0·1–24·7, p=0·048), equivalent to a difference of 15·3% (95% CI 5·3–25·3). All 15 patients received stimulation in the open-label phase. Overall, three serious adverse events occurred (two infections in DBS hardware at 2 and 7 weeks postoperatively, and one episode of deep-brain-stimulation-induced hypomania during the blinded on-stimulation period); all three resolved with treatment. GPi stimulation led to a significant improvement in tic severity, with an overall acceptable safety profile. Future research should concentrate on identifying the most effective target for DBS to control both tics and associated comorbidities, and further clarify factors that predict individual patient response. UK National Health Service.

Parkinson’s disease (PD) is one of the most prevalent neurological diseases, described by complex clinical phenotypes. The manifestations of PD include both motor and non-motor symptoms. We constituted an experimental protocol for the assessment of PD motor signs of lower extremities. Using a pair of sensor insoles, data were recorded from PD patients, Elderly and Adult groups. Assessment of PD patients has been performed by neurologists specialized in movement disorders using the Movement Disorder Society—Unified Parkinson’s Disease Rating Scale (MDS-UPDRS)-Part III: Motor Examination, on both ON and OFF medication states. Using as a reference point the quantified metrics of MDS-UPDRS-Part III, severity levels were explored by classifying normal, mild, moderate, and severe levels of PD. Elaborating the recorded gait data, 18 temporal and spatial characteristics have been extracted. Subsequently, feature selection techniques were applied to reveal the dominant features to be used for four classification tasks. Specifically, for identifying relations between the spatial and temporal gait features on: PD and non-PD groups; PD, Elderly and Adults groups; PD and ON/OFF medication states; MDS-UPDRS: Part III and PD severity levels. AdaBoost, Extra Trees, and Random Forest classifiers, were trained and tested. Results showed a recognition accuracy of 88%, 73% and 81% for, the PD and non-PD groups, PD-related medication states, and PD severity levels relevant to MDS-UPDRS: Part III ratings, respectively.

BackgroundDeep brain stimulation (DBS) can be an effective therapy for tics and comorbidities in select cases of severe, treatment-refractory Tourette syndrome (TS). Clinical responses remain variable across patients, which may be attributed to differences in the location of the neuroanatomical regions being stimulated. We evaluated active contact locations and regions of stimulation across a large cohort of patients with TS in an effort to guide future targeting.MethodsWe collected retrospective clinical data and imaging from 13 international sites on 123 patients. We assessed the effects of DBS over time in 110 patients who were implanted in the centromedial (CM) thalamus (n=51), globus pallidus internus (GPi) (n=47), nucleus accumbens/anterior limb of the internal capsule (n=4) or a combination of targets (n=8). Contact locations (n=70 patients) and volumes of tissue activated (n=63 patients) were coregistered to create probabilistic stimulation atlases.ResultsTics and obsessive–compulsive behaviour (OCB) significantly improved over time (p<0.01), and there were no significant differences across brain targets (p>0.05). The median time was 13 months to reach a 40% improvement in tics, and there were no significant differences across targets (p=0.84), presence of OCB (p=0.09) or age at implantation (p=0.08). Active contacts were generally clustered near the target nuclei, with some variability that may reflect differences in targeting protocols, lead models and contact configurations. There were regions within and surrounding GPi and CM thalamus that improved tics for some patients but were ineffective for others. Regions within, superior or medial to GPi were associated with a greater improvement in OCB than regions inferior to GPi.ConclusionThe results collectively indicate that DBS may improve tics and OCB, the effects may develop over several months, and stimulation locations relative to structural anatomy alone may not predict response. This study was the first to visualise and evaluate the regions of stimulation across a large cohort of patients with TS to generate new hypotheses about potential targets for improving tics and comorbidities.

The subthalamic nucleus (STN) is proposed to modulate response thresholds and speed–accuracy trade-offs. In situations of conflict, the STN is considered to raise response thresholds, allowing time for the accumulation of information to occur before a response is selected. Conversely, speed pressure is thought to reduce the activity of the STN and lower response thresholds, resulting in fast, errorful responses. In Parkinson’s disease (PD), subthalamic nucleus deep brain stimulation (STN-DBS) reduces the activity of the nucleus and improves motor symptoms. We predicted that the combined effects of STN stimulation and speed pressure would lower STN activity and lead to fast, errorful responses, hence resulting in impulsive action. We used the motion discrimination ‘moving-dots’ task to assess speed–accuracy trade-offs, under both speed and accuracy instructions. We assessed 12 patients with PD and bilateral STN-DBS and 12 age-matched healthy controls. Participants completed the task twice, and the patients completed it once with STN-DBS on and once with STN-DBS off, with order counterbalanced. We found that STN stimulation was associated with significantly faster reaction times but more errors under speed instructions. Application of the drift diffusion model showed that stimulation resulted in lower response thresholds when acting under speed pressure. These findings support the involvement of the STN in the modulation of speed–accuracy trade-offs and establish for the first time that speed pressure alone, even in the absence of conflict, can result in STN stimulation inducing impulsive action in PD.

Hypomimia is a prominent, levodopa-responsive symptom in Parkinson’s disease (PD). In our study, we aimed to distinguish ON and OFF dopaminergic medication state in a cohort of PD patients, analyzing their facial videos with a unique, interpretable Dual Stream Transformer model. Our approach integrated two streams of data: facial frame features and optical flow, processed through a transformer-based architecture. Various configurations of embedding dimensions, dense layer sizes, and attention heads were examined to enhance model performance. The final model, trained on 183 PD patients, attained an accuracy of 86% in differentiating between ON- and OFF-medication state. Moreover, uniform classification performance (up to 88%) was obtained across various stages of PD severity, as expressed by the Hoehn and Yahr (H&Y) scale. These values highlight the potential of our model as a non-invasive, cost-effective instrument for clinicians to remotely and accurately detect patients’ response to treatment from early to more advanced PD stages.