NMSs have been extensively studied in PD patients but not in other forms of parkinsonism such as Progressive Supranuclear Palsy (PSP). The primary objective of this study was to analyze the frequency, severity and the type of non-motor symptoms (NMS) in PSP patients using the non-motor symptoms scale (NMSS). The secondary objective was to differentiate NMS between PSP and Parkinson's disease (PD). We enrolled in this cross-sectional study 50 consecutive PSP and 100 matched Parkinson's disease (PD) patients, in the proportion PSP/PD = 1/2, matched in age, sex, and disease duration. Motor and Non Motor symptoms (different scales for each disease) were evaluated at baseline using PSP scale, SCOPA Motor, Montreal Cognitive Assessment (MOCA), HADS, Hamilton, and Non Motor Symptom scale (NMSS). Comparative analysis was done using chi-squared test, Mann-Whitney test and Fisher's exact test. Fifty PSP (56% female) and 100 PD (59% female) patients completed the study protocol and were included for statistical analysis. The NMSS total domains score in the PSP group was 77.58 ± 42.95 (range 14-163) with NMS burden grade: 4, very severe, and the in the PD group was 41.97 ± 35.45 (range: 0-215) with NMS burden grade: 3, severe. The comparative analysis showed that NMS total score ( < 0.0001), Sleep/Fatigue ( = 0.0007), Mood/Apathy ( = 0.0001), Gastrointestinal ( < 0.0001), and Urinary dysfunction ( = 0.0001) domains were significantly more severe in PSP patients than in PD. This observational study reports that NMSs are very frequent in PSP patients hence the higher burden of NMS in PSP specifically related to mood/apathy, attention/memory, gastrointestinal, urinary disturbances compared to PD.
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http://dx.doi.org/10.1038/s41531-017-0037-x | DOI Listing |
Sensors (Basel)
December 2024
Division of Neurological Rehabilitiation, Instituto Nacional de Rehabilitacion Luis Guillermo Ibarra Ibarra, Mexico City 14389, Mexico.
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December 2024
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
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December 2024
College of Computer and Information Sciences (CCIS), King Saud University, Riyadh 11543, Saudi Arabia.
One of the most promising applications for electroencephalogram (EEG)-based brain-computer interfaces (BCIs) is motor rehabilitation through motor imagery (MI) tasks. However, current MI training requires physical attendance, while remote MI training can be applied anywhere, facilitating flexible rehabilitation. Providing remote MI training raises challenges to ensuring an accurate recognition of MI tasks by healthcare providers, in addition to managing computation and communication costs.
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December 2024
Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA.
Mobility tasks like the Timed Up and Go test (TUG), cognitive TUG (cogTUG), and walking with turns provide insights into the impact of Parkinson's disease (PD) on motor control, balance, and cognitive function. We assess the test-retest reliability of these tasks in 262 PD participants and 50 controls by evaluating machine learning models based on wearable-sensor-derived measures and statistical metrics. This evaluation examines total duration, subtask duration, and other quantitative measures across two trials.
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December 2024
School of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia.
Traditional tactile brain-computer interfaces (BCIs), particularly those based on steady-state somatosensory-evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a promising alternative for generating tactile BCI control signals: somatosensory event-related potentials (sERPs). This study aimed to optimize the performance of a novel electrotactile BCI by employing advanced feature extraction and machine learning techniques on sERP signals for the classification of users' selective tactile attention.
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