Dorsolateral Prefrontal Cortex: A Possible Target for Modulating Dyskinesias in Parkinson's Disease by Repetitive Transcranial Magnetic Stimulation

We studied whether five sessions of 10 Hz repetitive transcranial magnetic stimulation (rTMS treatment) applied over the dorsolateral prefrontal cortex (DLPFC) or the primary motor cortex (MC) in advanced Parkinson's disease (PD) patients would have any effect on L-dopa-induced dyskinesias and cortical excitability. We aimed at a randomised, controlled study. Single-pulse transcranial magnetic stimulation (TMS), paired-pulse transcranial magnetic stimulation, and the Unified Parkinson's Disease Rating Scale (UPDRS parts III and IV) were performed prior to, immediately after, and one week after an appropriate rTMS treatment. Stimulation of the left DLPFC induced a significant motor cortex depression and a trend towards the improvement of L-dopa-induced dyskinesias

Concentration of apricot juice using complex membrane technology

In this study, pressed apricot (Prunus armeniaca L.) juice was concentrated using complex membrane technology with different module combinations: UF-RO-OD, UF-RO-MD, UF-NF-OD and UF-NF-MD. In case of the best combination a cross-flow polyethylene ultrafiltration membrane (UF) was applied for clarification, after which preconcentration was done using reverse osmosis (RO) with a polyamide membrane, and the final concentration was completed by osmotic distillation (OD) using a polypropylene module. The UF-RO-OD procedure resulted in a final concentrate with a 65-70 °Brix dry solid content and an excellent quality juice with high polyphenol content and high antioxidant capacity.Nanofiltration (NF) and membrane distillation (MD) were not proper economic solutions.The influence of certain operation parameters was examined experimentally. Temperatures of UF and RO were: 25, 30, and 35 °C, and of OD 25 °C. Recycle flow rates were: UF: 1, 1.5, and 2 m3 h−1; RO: 200, 400, and 600 l h−1; OD: 20, 30 and 40 l h−1. The flow rates in the module were expressed by the Reynolds number, as well. Based on preliminary experiments, the transmembrane pressures of UF and RO filtration were 4 bar and 50 bar, respectively. Each experimental run was performed three times. The following optimal operation parameters provided the lowest total cost: UF: 35 °C, 2 m3 h−1, 4 bar; RO: 35 °C, 600 l h−1, 50 bar; OD: 20, 30 and 40 l h−1; temperature 25 °C.In addition, experiments were performed for apricot juice concentration by evaporation, which technique is widely applied in the industry using vacuum and low temperature.For description the UF filtration, a dynamic model and regression by SPSS 14.0 statistics software were applied

Cortical dynamics and subcortical signatures of motor-language coupling in Parkinson’s disease

ABSTRACT: Impairments of action language have been documented in early stage Parkinson’s disease (EPD). The action-sentence compatibility effect (ACE) paradigm has revealed that EPD involves deficits to integrate action-verb processing and ongoing motor actions. Recent studies suggest that an abolished ACE in EPD reflects a cortico-subcortical disruption, and recent neurocognitive models highlight the role of the basal ganglia (BG) in motor-language coupling. Building on such breakthroughs, we report the first exploration of convergent cortical and subcortical signatures of ACE in EPD patients and matched controls. Specifically, we combined cortical recordings of the motor potential, functional connectivity measures, and structural analysis of the BG through voxelbased morphometry. Relative to controls, EPD patients exhibited an impaired ACE, a reduced motor potential, and aberrant frontotemporal connectivity. Furthermore, motor potential abnormalities during the ACE task were predicted by overall BG volume and atrophy. These results corroborate that motor-language coupling is mainly subserved by a cortico-subcortical network including the BG as a key hub. They also evince that action-verb processing may constitute a neurocognitive marker of EPD. Our findings suggest that research on the relationship between language and motor domains is crucial to develop models of motor cognition as well as diagnostic and intervention strategies

Cortical swallowing processing in early subacute stroke

<p>Abstract</p> <p>Background</p> <p>Dysphagia is a major complication in hemispheric as well as brainstem stroke patients causing aspiration pneumonia and increased mortality. Little is known about the recovery from dysphagia after stroke. The aim of the present study was to determine the different patterns of cortical swallowing processing in patients with hemispheric and brainstem stroke with and without dysphagia in the early subacute phase.</p> <p>Methods</p> <p>We measured brain activity by mean of whole-head MEG in 37 patients with different stroke localisation 8.2 +/- 4.8 days after stroke to study changes in cortical activation during self-paced swallowing. An age matched group of healthy subjects served as controls. Data were analyzed by means of synthetic aperture magnetometry and group analyses were performed using a permutation test.</p> <p>Results</p> <p>Our results demonstrate strong bilateral reduction of cortical swallowing activation in dysphagic patients with hemispheric stroke. In hemispheric stroke without dysphagia, bilateral activation was found. In the small group of patients with brainstem stroke we observed a reduction of cortical activation and a right hemispheric lateralization.</p> <p>Conclusion</p> <p>Bulbar central pattern generators coordinate the pharyngeal swallowing phase. The observed right hemispheric lateralization in brainstem stroke can therefore be interpreted as acute cortical compensation of subcortically caused dysphagia. The reduction of activation in brainstem stroke patients and dysphagic patients with cortical stroke could be explained in terms of diaschisis.</p

Neuroimaging-based classification of PTSD using data-driven computational approaches: a multisite big data study from the ENIGMA-PGC PTSD consortium

Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.Stress-related psychiatric disorders across the life spa