Single-channel EEG classification of sleep stages based on REM microstructure

Rapid-eye movement (REM) sleep, or paradoxical sleep, accounts for 20–25% of total night-time sleep in healthy adults and may be related, in pathological cases, to parasomnias. A large percentage of Parkinson's disease patients suffer from sleep disorders, including REM sleep behaviour disorder and hypokinesia; monitoring their sleep cycle and related activities would help to improve their quality of life. There is a need to accurately classify REM and the other stages of sleep in order to properly identify and monitor parasomnias. This study proposes a method for the identification of REM sleep from raw single-channel electroencephalogram data, employing novel features based on REM microstructures. Sleep stage classification was performed by means of random forest (RF) classifier, K-nearest neighbour (K-NN) classifier and random Under sampling boosted trees (RUSBoost); the classifiers were trained using a set of published and novel features. REM detection accuracy ranges from 89% to 92.7%, and the classifiers achieved a F-1 score (REM class) of about 0.83 (RF), 0.80 (K-NN), and 0.70 (RUSBoost). These methods provide encouraging outcomes in automatic sleep scoring and REM detection based on raw single-channel electroencephalogram, assessing the feasibility of a home sleep monitoring device with fewer channels

Speech impairment in Parkinson’s disease: acoustic analysis of unvoiced consonants in Italian native speakers

The study of the influence of Parkinson’s Disease (PD) on vocal signals has received much attention over the last decades. Increasing interest has been devoted to articulation and acoustic characterization of different phonemes. Method: In this study we propose the analysis of the Transition Regions (TR) of specific phonetic groups to model the loss of motor control and the difficulty to start/stop movements, typical of PD patients. For this purpose, we extracted 60 features from pre-processed vocal signals and used them as input to several machine learning models. We employed two data sets, containing samples from Italian native speakers, for training and testing. The first dataset - 28 PD patients and 22 Healthy Control (HC) - included recordings in optimal conditions, while in the second one - 26 PD patients and 18 HC- signals were collected at home, using non-professional microphones. Results: We optimized two support vector machine models for the application in controlled noise conditions and home environments, achieving 98% ± 1.1 and 88% ± 2.8 accuracy in 10-fold cross-validation, respectively. Conclusion: This study confirms the high capability of the TRs to discriminate between PD patients and healthy controls, and the feasibility of automatic PD assessment using voice recordings. Moreover, the promising performance of the implemented model discloses the option of voice processing using low-cost devices and domestic recordings, possibly self-managed by the patients themselves

G325R GBA mutation in Parkinson's disease: Disease course and long-term DBS outcome

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Deep Brain Stimulation Selection Criteria for Parkinson’s Disease: Time to Go beyond CAPSIT-PD

Despite being introduced in clinical practice more than 20 years ago, selection criteria for deep brain stimulation (DBS) in Parkinson’s disease (PD) rely on a document published in 1999 called ‘Core Assessment Program for Surgical Interventional Therapies in Parkinson’s Disease’. These criteria are useful in supporting the selection of candidates. However, they are both restrictive and out-of-date, because the knowledge on PD progression and phenotyping has massively evolved. Advances in understanding the heterogeneity of PD presentation, courses, phenotypes, and genotypes, render a better identification of good DBS outcome predictors a research priority. Additionally, DBS invasiveness, cost, and the possibility of serious adverse events make it mandatory to predict as accurately as possible the clinical outcome when informing the patients about their suitability for surgery. In this viewpoint, we analyzed the pre-surgical assessment according to the following topics: early versus delayed DBS; the evolution of the levodopa challenge test; and the relevance of axial symptoms; patient-centered outcome measures; non-motor symptoms; and genetics. Based on the literature, we encourage rethinking of the selection process for DBS in PD, which should move toward a broad clinical and instrumental assessment of non-motor symptoms, quantitative measurement of gait, posture, and balance, and in-depth genotypic and phenotypic characterization

Proteomic analysis of dopamine and \u3b1-synuclein interplay in a cellular model of Parkinson's disease pathogenesis

Altered dopamine homeostasis is an accepted mechanism in the pathogenesis of Parkinson\u2019s disease. a-Synuclein overexpression and impaired disposal contribute to this mechanism. However, biochemical alterations associated with the interplay of cytosolic dopamine and increased a-synuclein are still unclear. Catecholaminergic SH-SY5Y human neuroblastoma cells are a suitable model for investigating dopamine toxicity. In the present study, we report the proteomic pattern of SH-SY5Y cells overexpressing a-synuclein (1.6-fold induction) after dopamine exposure. Dopamine itself is able to upregulate a-synuclein expression. However, the effect is not observed in cells that already overexpress a-synuclein as a consequence of transfection. The proteomic analysis highlights significant changes in 23 proteins linked to specific cellular processes, such as cytoskeleton structure and regulation, mitochondrial function, energetic metabolism, protein synthesis, and neuronal plasticity. A bioinformatic network enrichment procedure generates a significant model encompassing all proteins and allows us to enrich functional categories associated with the combination of factors analyzed in the present study (i.e. dopamine together with a-synuclein). In particular, the model suggests a potential involvement of the nuclear factor kappa B pathway that is experimentally confirmed. Indeed, a-synuclein significantly reduces nuclear factor kappa B activation, which is completely quenched by dopamine treatment.Altered dopamine homeostasis is an accepted mechanism in the pathogenesis of Parkinson's disease. \u3b1-Synuclein overexpression and impaired disposal contribute to this mechanism. However, biochemical alterations associated with the interplay of cytosolic dopamine and increased \u3b1-synuclein are still unclear. Catecholaminergic SH-SY5Y human neuroblastoma cells are a suitable model for investigating dopamine toxicity. In the present study, we report the proteomic pattern of SH-SY5Y cells overexpressing \u3b1-synuclein (1.6-fold induction) after dopamine exposure. Dopamine itself is able to upregulate \u3b1-synuclein expression. However, the effect is not observed in cells that already overexpress \u3b1-synuclein as a consequence of transfection. The proteomic analysis highlights significant changes in 23 proteins linked to specific cellular processes, such as cytoskeleton structure and regulation, mitochondrial function, energetic metabolism, protein synthesis, and neuronal plasticity. A bioinformatic network enrichment procedure generates a significant model encompassing all proteins and allows us to enrich functional categories associated with the combination of factors analyzed in the present study (i.e. dopamine together with \u3b1-synuclein). In particular, the model suggests a potential involvement of the nuclear factor kappa B pathway that is experimentally confirmed. Indeed, \u3b1-synuclein significantly reduces nuclear factor kappa B activation, which is completely quenched by dopamine treatment. \ua9 2010 The Authors Journal compilation \ua9 2010 FEBS