UNASSIGNED: Spontaneous intracranial hypotension (SIH), a treatable condition that stems from spinal leakage of cerebrospinal fluid, usually presents with orthostatic headache, nausea, vomiting, dizziness, and tinnitus. A subset of patients, especially those with sagging of brain structures (\"brain sagging syndrome\"), develop several movement abnormalities. As SIH is treatable with epidural blood patch (EBP), movement disorders neurologists should be familiar with this syndrome.
UNASSIGNED: The authors performed a literature search in PubMed in July 2024 using the Boolean phrase- ((\"Brain sagging\")OR(\"Intracranial hypotension\"))AND((((((((((\"Movement disorders\")OR(\"Involuntary movements\"))OR(\"Tremor\"))OR(\"Dystonia\"))OR(\"Chorea\"))OR(\"Ballismus\"))OR(\"Myorhythmia\"))OR (\"Tic\"))OR(\"Ataxia\"))OR(\"Parkinsonism\")).
UNASSIGNED: We tabulated 21 case reports/series that highlighted the presence of movement disorders. The most reported phenomenology is gait unsteadiness. While it usually emerges in the background of the classic SIH symptoms, rarely, patients may present with isolated gait dysfunction. Tremor is the second most reported phenomenology with postural and kinetic tremor being the common subtypes. Holmes tremor has also been reported in SIH. Other reported phenomenologies are parkinsonism, chorea, and dystonia. One study reported a unique phenomenology i.e. compulsive repetitive flexion and breath holding in 35.3% of the patients. In majority of the patients, EBP resulted in substantial clinical and radiological improvement.
UNASSIGNED: Brain sagging syndrome due to SIH may present with a wide range of movement disorders. Mechanical distortion of the posterior fossa and subcortical structures result in the emergence of such movement abnormality. SIH adds to the list of conditions that result in \"treatable movement disorders.\" Therefore, movement disorders neurologists should be versed with the diagnosis and clinical features of this condition.

Lower limb exoskeleton rehabilitation robots are used to improve or restore the walking and movement ability of people with lower limb movement disorders. However, the required functions for patients differ based on various diseases. For example, patients with weak muscle strength require power assistance, patients with spinal cord injuries require motion compensation, patients with gait abnormalities require gait correction, and patients with strokes require neural rehabilitation. To design a more targeted lower limb exoskeleton rehabilitation robot for different diseases, this article summarised and compared existing lower limb exoskeleton rehabilitation robots according to their main functions and the characteristics and rehabilitation needs of various lower limb movement disorders. The correlations between the functions of existing devices and diseases were summarised to provide certain references for the development of new lower limb exoskeleton rehabilitation robots.
下肢外骨骼康复机器人应用于下肢运动功能障碍人群，使患者能够通过机器恢复或改善行走和运动能力。但是，基于不同疾病，患者所需求的功能是不同的，比如肌力不足的患者需要增强助力，脊髓损伤患者需要运动代偿，步态异常患者需要步态矫正，脑卒中患者需要神经康复。为了设计对疾病更有针对性的下肢外骨骼康复机器人，本文根据各类下肢功能障碍的特点与康复需求，按照设备所提供的主要功能，对现有的下肢外骨骼康复机器人进行汇总和分析比较，总结现有设备的功能与疾病的相关性，为研究设计新型下肢外骨骼康复机器人提供一定参考。.

Parkinson\'s Disease is a neurodegenerative disorder manifesting itself as a hypokinetic movement impairment with postural instability and gait disturbance. In case of failure and/or limited response, deep brain stimulation has been established as an alternative and effective treatment modality. However, a subset of PD patients with gait impairment represents a therapeutic challenge. A systematic review (2000-2023) was performed using PubMed, Embase, Web of Science, Scopus, and Cochrane Library databases to determine the efficacy, stimulation waveform/parameters, spine level, and outcome measures of spinal cord stimulation using different waveforms in PD patients with and without chronic pain. Spinal cord stimulation responsiveness was assessed within the pre-defined follow-up period in three groups (short-term follow-up = 0-3 months; intermediate follow-up = 3-12 months; and long-term follow-up = more than 12 months). In addition, we briefly outline alternative neurostimulation therapies and the most recent developments in closed-loop spinal cord stimulation relevant to PD. In summary, 18 publications and 70 patients from uncontrolled observational trials were included, with low-quality evidence and conflicting findings. First and foremost, the currently available data do not support the use of spinal cord stimulation to treat PD-related gait disorders but have confirmed its usefulness for PD-associated chronic pain.

Movement disorders such as cervical dystonia, blepharospasm, and hemifacial spasm negatively impact the quality of life of people living with these conditions. Botulinum toxin (BoNT) injections are commonly used to treat these disorders. We sought to describe patient characteristics, BoNT utilization, and potential adverse events (AEs) among patients with cervical dystonia, blepharospasm, and hemifacial spasm using Optum\'s de-identified Clinformatics® Data Mart Database. Patients were required to have a diagnosis of the specific condition plus evidence of treatment with BoNT between 8/1/2010 and 5/31/2022. Cervical dystonia patients were commonly females (76%) and aged 45 and older (78%); both blepharospasm and hemifacial spasm patients were commonly females (both 69%) and aged 65 and older (61% and 56%, respectively). Anticholinergics were commonly used (65-82% across cohorts), as were peripheral muscle relaxants for cervical dystonia patients specifically (31%). The median number of injections per year was 2 with the median weeks between injections being between 13 and 15. Of the AEs evaluated, dyspnea was identified frequently across all the cohorts (14-20%). The findings were similar for different BoNT formulations. More research is needed to thoroughly describe BoNT utilization, such as the doses injected, and to optimize treatment for patients with these conditions.

Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson\'s disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented mobility testing and subsequent data processing, as well as the added workload and complexity of this multi-step process. To simplify sensor-based mobility testing in diagnosing PD, we analyzed data from 262 PD participants and 50 controls performing several motor tasks wearing a sensor on their lower back containing a triaxial accelerometer and a triaxial gyroscope. Using ensembles of heterogeneous machine learning models incorporating a range of classifiers trained on a set of sensor features, we show that our models effectively differentiate between participants with PD and controls, both for mixed-stage PD (92.6% accuracy) and a group selected for mild PD only (89.4% accuracy). Omitting algorithmic segmentation of complex mobility tasks decreased the diagnostic accuracy of our models, as did the inclusion of kinesiological features. Feature importance analysis revealed that Timed Up and Go (TUG) tasks to contribute the highest-yield predictive features, with only minor decreases in accuracy for models based on cognitive TUG as a single mobility task. Our machine learning approach facilitates major simplification of instrumented mobility testing without compromising predictive performance.

Tremor, defined as an \"involuntary, rhythmic, oscillatory movement of a body part\", is a key feature of many neurological conditions including Parkinson\'s disease and essential tremor. Clinical assessment continues to be performed by visual observation with quantification on clinical scales. Methodologies for objectively quantifying tremor are promising but remain non-standardized across centers. Our center performs full-body behavioral testing with 3D motion capture for clinical and research purposes in patients with Parkinson\'s disease, essential tremor, and other conditions. The objective of this study was to assess the ability of several candidate processing pipelines to identify the presence or absence of tremor in kinematic data from patients with confirmed movement disorders and compare them to expert ratings from movement disorders specialists. We curated a database of 2272 separate kinematic data recordings from our center, each of which was contemporaneously annotated as tremor present or absent by a movement physician. We compared the ability of six separate processing pipelines to recreate clinician ratings based on F1 score, in addition to accuracy, precision, and recall. The performance across algorithms was generally comparable. The average F1 score was 0.84±0.02 (mean ± SD; range 0.81-0.87). The second highest performing algorithm (cross-validated F1=0.87) was a hybrid that used engineered features adapted from an algorithm in longstanding clinical use with a modern Support Vector Machine classifier. Taken together, our results suggest the potential to update legacy clinical decision support systems to incorporate modern machine learning classifiers to create better-performing tools.

Background: The postmortem diagnostic of individuals having suffered presumptive neurodegenerative disease comprises exclusion of a prion disease, extensive brain sampling and histopathological evaluation, which are resource-intensive and time consuming. To exclude prion disease and to achieve prompt accurate preliminary diagnosis, we developed a fast-track procedure for the histopathological assessment of brains from patients with suspected neurodegenerative disease. Methods: Based on the screening of two brain regions (frontal cortex and cerebellum) with H&E and six immunohistochemical stainings in 133 brain donors, a main histopathological diagnosis was established and compared to the final diagnosis made after a full histopathological work-up according to our brain bank standard procedure. Results: In over 96 % of cases there was a concordance between the fast-track and the final main neuropathological diagnosis. A prion disease was identified in four cases without prior clinical suspicion of a prion infection. Conclusion: The fast-track screening approach relying on two defined, easily accessible brain regions is sufficient to obtain a reliable tentative main diagnosis in individuals with neurodegenerative disease and thus allows for a prompt feedback to the physicians. However, a more thorough histological work-up taking into account the clinical history and the working diagnosis from fast-track screening is necessary for accurate staging and for assessment of co-pathologies.

UNASSIGNED: The Montreal Cognitive Assessment (MoCA) is recommended by the Movement Disorder Society for cognitive testing in movement disorders including Parkinson\'s disease (PD) and lewy body dementia. Few studies have compared cognitive screening instruments in these diseases, which overlap clinically.
UNASSIGNED: To compare the MoCA and Quick Mild Cognitive Impairment (Qmci) screen in this population.
UNASSIGNED: Patients attending memory and movement disorder clinics associated with a university hospital had the MoCA and Qmci screen performed and diagnostic accuracy compared with the area under the receiver operating characteristic curve (AUC). Duration and severity of movement disorders was assessed using the Unified PD Rating Scale (UPDRS).
UNASSIGNED: In total, 133 assessments were available, median age 74±5. Median education was 11±4 years and 65% were male. Median total UPDRS score was 37±26. Median Qmci screen was 51±27, median MoCA was 19±10. There were statistically significant differences in test scores between those with subjective symptoms but normal cognition, mild cognitive impairment (MCI) and dementia (p < 0.001). The Qmci screen had significantly greater accuracy differentiating normal cognition from MCI versus the MoCA (AUC 0.90 versus 0.72, p = 0.01). Both instruments had similar accuracy in identifying cognitive impairment and separating MCI from dementia. The median administration time for the Qmci screen and MoCA were 5.19 and 9.24 minutes (p < 0.001), respectively.
UNASSIGNED: Both the MoCA and Qmci screen have good to excellent accuracy in a population with movement disorders experiencing cognitive symptoms. The Qmci screen was significantly more accurate for those with early symptoms and had a shorter administration time.

BACKGROUND: Transcranial sonography (TCS) plays a crucial role in diagnosing Parkinson\'s disease. However, the intricate nature of TCS pathological features, the lack of consistent diagnostic criteria, and the dependence on physicians\' expertise can hinder accurate diagnosis. Current TCS-based diagnostic methods, which rely on machine learning, often involve complex feature engineering and may struggle to capture deep image features. While deep learning offers advantages in image processing, it has not been tailored to address specific TCS and movement disorder considerations. Consequently, there is a scarcity of research on deep learning algorithms for TCS-based PD diagnosis.
METHODS: This study introduces a deep learning residual network model, augmented with attention mechanisms and multi-scale feature extraction, termed AMSNet, to assist in accurate diagnosis. Initially, a multi-scale feature extraction module is implemented to robustly handle the irregular morphological features and significant area information present in TCS images. This module effectively mitigates the effects of artifacts and noise. When combined with a convolutional attention module, it enhances the model\'s ability to learn features of lesion areas. Subsequently, a residual network architecture, integrated with channel attention, is utilized to capture hierarchical and detailed textures within the images, further enhancing the model\'s feature representation capabilities.
RESULTS: The study compiled TCS images and personal data from 1109 participants. Experiments conducted on this dataset demonstrated that AMSNet achieved remarkable classification accuracy (92.79%), precision (95.42%), and specificity (93.1%). It surpassed the performance of previously employed machine learning algorithms in this domain, as well as current general-purpose deep learning models.
CONCLUSIONS: The AMSNet proposed in this study deviates from traditional machine learning approaches that necessitate intricate feature engineering. It is capable of automatically extracting and learning deep pathological features, and has the capacity to comprehend and articulate complex data. This underscores the substantial potential of deep learning methods in the application of TCS images for the diagnosis of movement disorders.

BACKGROUND: Tourette syndrome is a neurodevelopmental movement disorder involving basal ganglia dysfunction. PDE10A inhibitors modulate signaling in the striatal basal ganglia nuclei and are thus of interest as potential therapeutics in treating Tourette syndrome and other movement disorders.
METHODS: The preclinical pharmacology and toxicology, human safety and tolerability, and human PET striatal enzyme occupancy data for the PDE10A inhibitor EM-221 are presented.
RESULTS: EM-221 inhibited PDE10A with an in vitro IC50 of 9 pM and was >100,000 selective vs. other PDEs and other CNS receptors and enzymes. In rats, at doses of 0.05-0.50 mg/kg, EM-221 reduced hyperlocomotion and the disruption of prepulse inhibition induced by MK-801, attenuated conditioned avoidance, and facilitated novel object recognition, consistent with PDE10A\'s inhibition. EM-221 displayed no genotoxicity and was well tolerated up to 300 mg/kg in rats and 100 mg/kg in dogs. In single- and multiple-day ascending dose studies in healthy human volunteers, EM-221 was well tolerated up to 10 mg, with a maximum tolerated dose of 15 mg. PET imaging indicated that a PDE10A enzyme occupancy of up to 92.8% was achieved with a ~24 h half-life.
CONCLUSIONS: The preclinical and clinical data presented here support the study of EM-221 in phase 2 trials of Tourette syndrome and other movement disorders.