BACKGROUND: Stimulation-induced dysarthria (SID) is a troublesome and potentially therapy-limiting side effect of deep brain stimulation of the subthalamic nucleus (STN-DBS) in patients with Parkinson\'s disease (PD). To date, the origin of SID, and especially whether there is an involvement of cerebellar pathways as well as the pyramidal tract, remains a matter of debate. Therefore, this study aims to shed light on structural networks associated with SID and to derive a data-driven model to predict SID in patients with PD and STN-DBS.
METHODS: Randomised, double-blinded monopolar reviews determining SID thresholds were conducted in 25 patients with PD and STN-DBS. A fibre-based mapping approach, implementing the calculation of fibr-wise ORs for SID, was employed to identify the distributional pattern of SID in the STN\'s vicinity. The ability of the data-driven model to classify stimulation volumes as \'causing SID\' or \'not causing SID\' was validated by calculating receiver operating characteristics (ROC) in an independent out-of-sample cohort comprising 14 patients with PD and STN-DBS.
RESULTS: Local fibre-based stimulation maps showed an involvement of fibres running lateral and posteromedial to the STN in the pathogenesis of SID, independent of the investigated hemisphere. ROC analysis in the independent out-of-sample cohort resulted in a good fit of the data-driven model for both hemispheres (area under the curve (AUC)left=0.88, AUCright=0.88).
CONCLUSIONS: This study reveals an involvement of both, cerebello-thalamic fibres, as well as the pyramidal tract, in the pathogenesis of SID in STN-DBS. The results may impact future postoperative programming strategies to avoid SID in patients with PD and STN-DBS TRIAL REGISTRATION NUMBER: DRKS00023221; German Clinical Trials Register (DRKS) Number.

OBJECTIVE: Although previous studies have described phenomenological diagnoses, they lacked description of aetiological spectrum in patients visiting movement disorders (MD) service. Herein, we classify the MD phenomenology and describe aetiology wise distribution of each phenomenology in patients visiting a tertiary care movement disorders service.
METHODS: Collected information included demographic profile (age of onset, age at presentation, gender, duration of illness before presentation), predominant MD phenomenology [such as parkinsonism, dystonia, ataxia, tremor, chorea, ballism, myoclonus, tics, stereotypy, restless legs syndrome (RLS) and others], diagnostic evaluations and detected aetiology.
RESULTS: This observational study included 1140 MD patients over a span of 5 years. The median (IQR) age of onset was 49 (35-60) years and age at presentation was 54 (40-65) years, with median duration of illness being 36 (18-72) months. Nearly two-third of patients were males (M:F=731:409). Parkinsonism (n=494, 43.3 %) was the most common MD phenomenology observed, followed by dystonia (n=219, 19.2 %), ataxia (n=125, 11 %), tremor (n=118, 10.4 %), myoclonus (n=73, 6.4 %), chorea (n=40, 3.5 %), spasticity (n=22, 1.9 %), tics (n=8, 0.7 %), and RLS (n=8, 0.7 %). Thirty-three (2.9 %) patients were grouped under miscellaneous MDs. Overall, neurodegenerative disorders (57.4 %) were the most common cause of MDs. Parkinson\'s disease, genetic dystonia, essential tremor, genetic ataxias, hemifacial spasm, and Huntington\'s disease were the most common aetiologies for parkinsonism, dystonia, tremor, ataxia, myoclonus, and chorea, respectively.
CONCLUSIONS: Parkinsonism was the most common phenomenology observed in MD patients, and was followed by dystonia, ataxia and tremor. Neurodegenerative disorders were the most common aetiology detected.

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.