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Motor fluctuations in Parkinson's disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefits on symptoms, including bradykinesia and rigidity. These fluctuations significantly impair the quality of life of many Parkinson's patients. However, current clinical evaluation tools are not designed for the continuous, naturalistic (real-world) symptom monitoring needed to optimize clinical therapy to treat fluctuations. Although commercially available wearable motor monitoring, used over multiple days, can augment neurological decision making, the feasibility of rapid and dynamic detection of motor fluctuations is unclear. So far, applied wearable monitoring algorithms are trained on group data. In this study, we investigated the influence of individual model training on short timescale classification of naturalistic bradykinesia fluctuations in Parkinson's patients using a single-wrist accelerometer. As part of the Parkinson@Home study protocol, 20 Parkinson patients were recorded with bilateral wrist accelerometers for a one hour OFF medication session and a one hour ON medication session during unconstrained activities in their own homes. Kinematic metrics were extracted from the accelerometer data from the bodyside with the largest unilateral bradykinesia fluctuations across medication states. The kinematic accelerometer features were compared over the 1 h duration of recording, and medication-state classification analyses were performed on 1 min segments of data. Then, we analyzed the influence of individual versus group model training, data window length, and total number of training patients included in group model training, on classification. Statistically significant areas under the curves (AUCs) for medication induced bradykinesia fluctuation classification were seen in 85% of the Parkinson patients at the single minute timescale using the group models. Individually trained models performed at the same level as the group trained models (mean AUC both 0.70, standard deviation respectively 0.18 and 0.10) despite the small individual training dataset. AUCs of the group models improved as the length of the feature windows was increased to 300 s, and with additional training patient datasets. We were able to show that medication-induced fluctuations in bradykinesia can be classified using wrist-worn accelerometry at the time scale of a single minute. Rapid, naturalistic Parkinson motor monitoring has the clinical potential to evaluate dynamic symptomatic and therapeutic fluctuations and help tailor treatments on a fast timescale.

Background: With advances in clinical genetic testing, associations between genetic neurodevelopmental disorders and parkinsonism are increasingly recognized. In this review, we aimed to provide a comprehensive overview of reports on parkinsonism in genetic neurodevelopmental disorders and summarize findings related to genetic diagnosis, clinical features and proposed disease mechanisms. Methods: A systematic literature review was conducted in PubMed and Embase on June 15, 2021. Search terms for parkinsonism and genetic neurodevelopmental disorders, using generic terms and the Human Phenotype Ontology, were combined. Study characteristics and descriptive data were extracted from the articles using a modified version of the Cochrane Consumers and Communication Review Group's data extraction template. The protocol was registered in PROSPERO (CRD42020191035). Results: The literature search yielded 208 reports for data-extraction, describing 69 genetic disorders in 422 patients. The five most reported from most to least frequent were: 22q11.2 deletion syndrome, beta-propeller protein-associated neurodegeneration, Down syndrome, cerebrotendinous xanthomatosis, and Rett syndrome. Notable findings were an almost equal male to female ratio, an early median age of motor onset (26 years old) and rigidity being more common than rest tremor. Results of dopaminergic imaging and response to antiparkinsonian medication often supported the neurodegenerative nature of parkinsonism. Moreover, neuropathology results showed neuronal loss in the majority of cases. Proposed disease mechanisms included aberrant mitochondrial function and disruptions in neurotransmitter metabolism, endosomal trafficking, and the autophagic-lysosomal and ubiquitin-proteasome system. Conclusion: Parkinsonism has been reported in many GNDs. Findings from this study may provide clues for further research and improve management of patients with GNDs and/or parkinsonism. [ABSTRACT FROM AUTHOR]

Background: The ventral intermediate nucleus (VIM) is the most commonly used target for deep brain stimulation (DBS) in patients with essential tremor (ET). Recent evidence suggests that the posterior subthalamic area (PSA) might be a better target for tremor reduction. We compared the outcome of VIM DBS with PSA DBS in our cohort of patients. Methods: Overall, 19 ET patients with bilateral DBS were included in this retrospective study, with a total of 38 electrodes (12 located in the VIM, 12 in the PSA, and 14 in an intermediate area). The outcome was measured using the essential tremor rating scale (ETRS), the glass scale and the quality of life in essential tremor questionnaire (QUEST). Results: Unilateral tremor-scores with items 5-6 (tremor of the upper extremity), 8-9 (tremor of the lower extremity), and 11-14 (hand function) from the ETRS showed a 63% tremor reduction in the VIM group, 47% tremor reduction in the PSA group, and 67% tremor reduction in the intermediate group after a mean follow-up of 1.6 years. After a mean follow-up of 5.8 years, there was a tremor reduction of 50%, 34%, and 45%, respectively. In our series, side effects such as dysarthria (75%), ataxia and disequilibrium (40%), and paraesthesia (15%) were assessed. Conclusions: All aforementioned anatomical target areas are effective in reducing tremor, although no superior reduction was found with PSA stimulation. Because of intraindividual differences between left and right hemisphere regarding the stimulated anatomical target, no conclusions can be drawn regarding differences in side effects. [ABSTRACT FROM AUTHOR]

Background: Around 50% of Parkinson's disease patients experience motor fluctuations after long-term treatment with levodopa. These fluctuations may be accompanied by mood fluctuations. Routine cross-sectional assessments cannot capture the extent of these motor and mood fluctuations and their possible associations. Experience sampling techniques that use frequently repeated measurements of symptoms over time are able to capture such fluctuations. Based on such data, longitudinal associations between symptoms can be studied using network analysis.Aim: The purpose of this study is to identify longitudinal associations between motor symptoms and mood states in a patient with Parkinson's disease.Methods: A 53-year-old man with Parkinson's disease and motor fluctuations collected experience sampling data during 34 consecutive days. A set of dependent variables included tremor, rigidity, balance problems, and "on/off" state, and the mood variables anxiety, cheerful, and "down." Independent variables were the same variables assessed at the preceding measurement. Regression coefficients were calculated and presented in a network graph.Results: In this patient, anxiety and cheerfulness had a central position within the symptom network. Higher anxiety was prospectively associated with increased rigidity and tremor and with feeling "down." Cheerfulness was associated with less tremor. Balance problems were not influenced by cheerfulness nor anxiety, but increased balance problems were associated with reduced cheerfulness at the next assessment. Feeling "down" did not influence self-reported motor symptom severity at the next assessment.Conclusion: This n = 1 study shows that network analysis of experience sampling data may reveal longitudinal associations of self-reported motor symptoms and mood states that may have relevance for treatment strategies. © 2018 International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]