RESUMO
Background: While cannabis-based medicine is being commonly used in patients with movement disorders, there is a scarcity of publications regarding the effect of cannabis on dystonia. We aimed to describe medical cannabis use in patients with dystonia and related pain. Methods: We employed a structured interview to obtain data on the cannabis treatment regimen, perception of effectiveness and side effect profile. Eligible participants were patients diagnosed with dystonia from the movement disorders unit at the Tel-Aviv Medical Center who had used licensed medical cannabis between January 2019 and January 2021. Results: Twenty-three subjects were interviewed (11 women, mean age 52.7). The most common way of administration was smoking (n = 11). Following an average of 2.5 ± 2.9 years of use, those with widespread dystonia (generalized, hemi and multifocal, n = 11) self-reported on a numeric rating scale an average 63% (range 0%-100%) reduction in symptoms of dystonia, while those with more focal dystonia patterns reported a significantly lower treatment effect of 32%. Participants reported a positive impact in related pain and quality of life, with an average rating of 3.8 out of 5 (SD = 1.2, median = 4) and 3.6 out of 5 (SD = 1.15, median = 4), respectively. Most common side effects were dry mouth (65%), sedation (43%), dizziness (39%) and psychiatric disorders (26%). Three patients (13%) discontinued therapy. Conclusion: A subset of dystonia patients who use medical cannabis under clinical observation reported significant subjective improvement during 30 months of use in average. Further prospective randomized controlled trials are required to examine the effectiveness of cannabis in dystonia.
RESUMO
Background: Parkinson's disease (PD) often presents with subtle early signs, making diagnosis difficult. F-DOPA PET imaging provides a reliable measure of dopaminergic function and is a primary tool for early PD diagnosis. This study aims to evaluate the ability of machine-learning (ML) extracted EEG features to predict F-DOPA results and distinguish between PD and non-PD patients. These features, extracted using a single-channel EEG during an auditory cognitive assessment, include EEG feature A0 associated with cognitive load in healthy subjects, and EEG feature L1 associated with cognitive task differentiation. Methods: Participants in this study are comprised of cognitively healthy patients who had undergone an F-DOPA PET scan as a part of their standard care (n = 32), and cognitively healthy controls (n = 20). EEG data collected using the Neurosteer system during an auditory cognitive task, was decomposed using wavelet-packet analysis and machine learning methods for feature extraction. These features were used in a connectivity analysis that was applied in a similar manner to fMRI connectivity. A preliminary model that relies on the features and their connectivity was used to predict initially unrevealed F-DOPA test results. Then, generalized linear mixed models (LMM) were used to discern between PD and non-PD subjects based on EEG variables. Results: The prediction model correctly classified patients with unrevealed scores as positive F-DOPA. EEG feature A0 and the Delta band revealed distinct activity patterns separating between study groups, with controls displaying higher activity than PD patients. In controls, EEG feature L1 showed variations between resting state and high-cognitive load, an effect lacking in PD patients. Conclusion: Our findings exhibit the potential of single-channel EEG technology in combination with an auditory cognitive assessment to distinguish positive from negative F-DOPA PET scores. This approach shows promise for early PD diagnosis. Additional studies are needed to further verify the utility of this tool as a potential biomarker for PD.
