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Opis: Background and purpose: The aim of this study was to examine the risk factors, prodromal symptoms, non-motor symptoms (NMS), and motor symptoms (MS) in different Parkinson’s disease (PD) non-motor subtypes, classified using newly established criteria and a data-driven approach. Methods: A total of 168 patients with idiopathic PD underwent comprehensive NMS and MS examinations. NMS were assessed by the Non-Motor Symptom Scale (NMSS), Montreal Cognitive Assessment (MoCA), Hamilton Depression Scale (HAM-D), Hamilton Anxiety Rating Scale (HAM-A), REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ), Epworth Sleepiness Scale (ESS), Starkstein Apathy Scale (SAS) and Fatigue Severity Scale (FSS). Motor subtypes were classified based on Stebbins’ method. Patients were classified into groups of three NMS subtypes (cortical, limbic, and brainstem) based on the newly designed inclusion criteria. Further, data-driven clustering was performed as an alternative, statistical learning-based classification approach. The two classification approaches were compared for consistency. Results: We identified 38 (22.6%) patients with the cortical subtype, 48 (28.6%) with the limbic, and 82 (48.8%) patients with the brainstem NMS PD subtype. Using a data-driven approach, we identified five different clusters. Three corresponded to the cortical, limbic, and brainstem subtypes, while the two additional clusters may have represented patients with early and advanced PD. Pearson chi-square test of independence revealed that a priori classification and cluster membership were significantly related to one another with a large effect size (χ2(8) = 175.001, p < 0.001, Cramer’s V = 0.722). The demographic and clinical profiles differed between NMS subtypes and clusters. Conclusion: Using the actuarial and clustering approach, marked differences between individual NMS subtypes were found. The newly established criteria have potential as a simplified tool for future clinical research of NMS subtypes of Parkinson’s disease.
Ključne besede: Parkinson’s disease, non-motor symptoms subtypes, a priori classification, cluster analysis
Objavljeno v DKUM: 07.04.2025; Ogledov: 0; Prenosov: 9
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Opis: Parkinson’s disease (PD) is generally associated with abnormally increased beta band oscillations in the cortico-basal ganglia loop during walking. PD patients with freezing of gait (FOG) exhibit a more distinct, prolonged narrow band of beta oscillations that are locked to the initiation of movement at ∼18 Hz. Upon initiation of cycling movements, this oscillation has been reported to be weaker and rather brief in duration. Due to the suppression of the overall beta band power during cycling and its continuous nature of the movement, cycling is considered to be less demanding for cortical networks compared to walking, including reduced need for sensorimotor processing, and thus unimpaired continuous cycling motion. Furthermore, cycling has been considered one of the most efficient non-pharmacological therapies with an influence on the subthalamic nucleus (STN) beta rhythms implicative of the deep brain stimulation effects. In the current review, we provide an overview of the currently available studies and discuss the underlying mechanism of preserved cycling ability in relation to the FOG in PD patients. The mechanisms are presented in detail using a graphical scheme comparing cortical oscillations during walking and cycling in PD.
Ključne besede: gait, freezing of gait, Parkinson's disease, cycling, cortical oscillations, beta band
Objavljeno v DKUM: 04.12.2024; Ogledov: 0; Prenosov: 9
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Opis: Pathological tremor is a common but highly complex movement disorder, affecting ~5% of population older than 65 years. Different methodologies have been proposed for its quantification. Nevertheless, the discrimination between Parkinson's disease tremor and essential tremor remains a daunting clinical challenge, greatly impacting patient treatment and basic research. Here, we propose and compare several movement-based and electromyography-based tremor quantification metrics. For the latter, we identified individual motor unit discharge patterns from high-density surface electromyograms and characterized the neural drive to a single muscle and how it relates to other affected muscles in 27 Parkinson's disease and 27 essential tremor patients. We also computed several metrics from the literature. The most discriminative metrics were the symmetry of the neural drive to muscles, motor unit synchronization, and the mean log power of the tremor harmonics in movement recordings. Noteworthily, the first two most discriminative metrics were proposed in this study. We then used decision tree modelling to find the most discriminative combinations of individual metrics, which increased the accuracy of tremor type discrimination to 94%. In summary, the proposed neural drive-based metrics were the most accurate at discriminating and characterizing the two most common pathological tremor types.
Ključne besede: Parkinson's disease, essential tremor, electromyography, wrist movements, motor units, muscular excitation, decision tree
Objavljeno v DKUM: 03.11.2017; Ogledov: 1699; Prenosov: 465
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