BACKGROUND: According to the current Parkinson\'s Disease (PD) pathogenesis hypotheses, the vagus nerve (VN) is essential for disease development. It has been identified as a main entry point for misfolded α-synuclein to the central nervous system, and surgical vagotomy appears to limit disease progress both in animal models and in humans. A recent approach tried to assess VN size in PD patients via neck ultrasonography, but the clinical value of this method is yet to be established.
BACKGROUND: A systematic search of the MEDLINE, Scopus, and Web of Science databases was conducted, and 12 case- -control studies were included. Meta-analysis revealed a modest reduction in VN size in PD (effect size - 0.79 SD (95%CI [-1.34, -0.25] p = 0.004)). The atrophy was more pronounced on the right side, and the nerve was smaller in females. In PD patients, VN reduction correlated with cardiac parasympathetic function decline and with advances in motor ratings. The discrimination potential for PD diagnosis, and any association with other non-motor domains, remains unclear.
CONCLUSIONS: VN atrophy in PD could be detected by ultrasound imaging. However, the clinical significance of this phenomenon has yet to be clarified. Size reduction is not readily apparent and is individually variable. However, it may be considered a promising means to improve early PD diagnosis and the recognition of autonomic dysfunction.
CONCLUSIONS: With more extensive research, VN sonography could provide useful evidence regarding disease origins. Imaging should be performed together with a profound clinical assessment and biomarker testing to establish the role to be played by this method in future practice.

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: Diagnostic criteria for progressive supranuclear palsy (PSP) include midbrain atrophy in MRI and hypometabolism in [18F]fluorodeoxyglucose (FDG)-positron emission tomography (PET) as supportive features. Due to limited data regarding their relative and sequential value, there is no recommendation for an algorithm to combine both modalities to increase diagnostic accuracy. This study evaluated the added value of sequential imaging using state-of-the-art methods to analyse the images regarding PSP features.
METHODS: The retrospective study included 41 PSP patients, 21 with Richardson\'s syndrome (PSP-RS), 20 with variant PSP phenotypes (vPSP) and 46 sex- and age-matched healthy controls. A pretrained support vector machine (SVM) for the classification of atrophy profiles from automatic MRI volumetry was used to analyse T1w-MRI (output: MRI-SVM-PSP score). Covariance pattern analysis was applied to compute the expression of a predefined PSP-related pattern in FDG-PET (output: PET-PSPRP expression score).
RESULTS: The area under the receiver operating characteristic curve for the detection of PSP did not differ between MRI-SVM-PSP and PET-PSPRP expression score (p≥0.63): about 0.90, 0.95 and 0.85 for detection of all PSP, PSP-RS and vPSP. The MRI-SVM-PSP score achieved about 13% higher specificity and about 15% lower sensitivity than the PET-PSPRP expression score. Decision tree models selected the MRI-SVM-PSP score for the first branching and the PET-PSPRP expression score for a second split of the subgroup with normal MRI-SVM-PSP score, both in the whole sample and when restricted to PSP-RS or vPSP.
CONCLUSIONS: FDG-PET provides added value for PSP-suspected patients with normal/inconclusive T1w-MRI, regardless of PSP phenotype and the methods to analyse the images for PSP-typical features.

OBJECTIVE: This article reviews the clinical and antibody spectrum of autoimmune cerebellar ataxia and other autoimmune movement disorders. It highlights characteristic phenotypes and red flags to the diagnosis and how these rare, but treatable, disorders are integrated into a differential diagnosis.
UNASSIGNED: An increasing number of neuronal antibodies have been identified in patients with cerebellar ataxia, for example, against Kelch-like protein 11 (KLHL11), seizure-related 6 homolog-like 2, septin-3 and septin-5, or tripartite motif containing protein 9 (TRIM9), TRIM46, and TRIM67. Ig-like cell adhesion molecule 5 (IgLON5) antibody-associated syndromes have emerged as an important alternative diagnostic consideration to various neurodegenerative diseases such as Huntington disease or atypical parkinsonism. Opsoclonus-myoclonus syndrome emerged as the most relevant parainfectious movement disorder related to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
UNASSIGNED: Autoimmune cerebellar ataxia and other autoimmune movement disorders encompass a broad spectrum of different clinical syndromes, antibodies, and immunopathophysiologic mechanisms. Clinical acumen is key to identifying the cases that should undergo testing for neuronal antibodies. Given the overlap between phenotypes and antibodies, panel testing in serum and CSF is recommended.

This opinion piece describes major limitations of using α-synuclein in speculative neuronally enriched for diagnosing or predicting Parkinson\'s disease risk from prodromal conditions such as REM behaviour disorder. It concludes that such an approach is unreliable and recommends that future researchers divert away to more widely accepted approaches such as seed amplification assays.

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: Consultation-liaison (CL) psychiatrists are frequently asked to consult on various abnormal movements(1). CL psychiatrists can be instrumental in aiding the primary teams to identify and manage these movement disorders. In this manuscript, we provide an illustrative case of a patient presenting with myoclonus and offer a review on this important topic. Myoclonus accompanied by delirium represents a rare post-transplant complication and can be associated with heightened morbidity and mortality. The incidence of this complication in solid organ transplant (SOT) recipients is scarcely documented, and its pathophysiology remains inadequately understood. Potential etiologies in the intensive care unit (ICU) are numerous and likely multifactorial. The literature lacks detailed descriptions of the correlation and association between myoclonus and uremia. Management of this condition requires a multimodal approach, focusing on resolving underlying metabolic disturbances and providing symptomatic treatment.
OBJECTIVE: This manuscript describes the clinical presentation of myoclonus in a liver transplant recipient accompanied by delirium and precipitated by uremia. We aim to highlight the diagnostic and therapeutic complexities, help providers distinguish myoclonus from other movement disorders, and aid appropriate management.
RESULTS: We present a case of acute myoclonus in an elderly female liver transplant recipient precipitated by uremia and improved after continuous renal replacement treatment. In addition, we conducted a systematic review utilizing EMBASSE and PubMed of reported cases of myoclonus, delirium, and/or encephalopathy accompanied by uremia. We included 12 manuscripts in our review and discussed their findings.
CONCLUSIONS: CL psychiatrists are frequently consulted for a range of movement disorders in the ICU, including myoclonus. Accurate diagnosis and identification of contributing etiologies are critical in these cases. Management typically involves addressing the underlying disorder, such as using dialysis for uremia, alongside symptomatic treatment with benzodiazepines to mitigate the frequency and amplitude of myoclonus. This approach helps to alleviate both the physical burden and psychological distress associated with the condition. This case underscores the pivotal role of the CL psychiatrist within a complex multidisciplinary team, contributing to diagnostic precision and optimization of management strategies for movement disorders.