Larkinharmon2866

The coupling between neuronal activity and cerebral blood flow (CBF), known as neurovascular coupling, has been reported to be impaired after stroke. This study aims to investigate the neurovascular coupling impairment at the acute stage after ischemic stroke. Laser speckle contrast imaging (LSCI) was applied to measure the hemodynamic response to optogenetic excitation of sensorimotor neurons in healthy and ischemic brain. The results showed that the hemodynamic response to optogenetic stimulation decreased and the regional CBF response was correlated with the distance from the ischemic core at the acute stage, regardless of the change in resting CBF. Our results also demonstrated that excitatory neuronal stimulation of intact area could promote the recovery of neurovascular coupling, whereas peri-infarct neuronal excitation failed to restore neurovascular function 24 hrs after ischemia. These results suggested the intact periphery of penumbra as the target for excitatory stimulation in aspect of restoring the perfusion after ischemic stroke.Platelet and fibrin-rich blood clots can respond differently to red blood cell rich clots during ischemic stroke treatment, which includes thrombolysis and mechanical thrombectomy. Currently, there is no accurate way to identify the type of clot in advance of treatment. Pyrvinium cost If the type of blood clot can be identified, the optimum clot removal process can be chosen and patient outcomes can be improved. In this paper we fabricate physiologically relevant blood clot analogues from human blood, that cover a range of red blood cell, fibrin, and platelet concentrations. We characterize the dielectric profile of these formed clots using an open-ended coaxial probe method across a wide frequency range. After the dielectric measurements are completed, histology on each blood clot is performed to determine the concentration of red blood cells present. In total, 32 unique blood clots were measured.With this completed analysis, we investigate the correlation between the dielectric properties across this frequency range and the red blood cell count of the formed blood clots. Furthermore, we develop a model to predict whether an unknown blood clot can be categorized as red blood cell rich or platelet and fibrin-rich based solely on the measured dielectric properties.Clinical Relevance-Using the dielectric profile of a clot we can predict whether a clot is platelet and fibrin-rich or red blood cell rich allowing clinicians to more easily determine treatment methods during an intervention for ischemic stroke.Stroke is a major cause of long-term disability. Because patients recovering from stroke often perform differently in clinical settings than in their naturalistic environments, remote monitoring of motor performance is needed to evaluate the true impact of prescribed therapies. Wearable sensors have been considered as a technical solution to this problem, but most existing systems focus on measuring the amount of movement without considering the quality of movement. We present a novel method to seamlessly and unobtrusively measure the quality of individual reaching movements by leveraging a motor control theory that describes how the central nervous system plans and executes movements. We trained and evaluated our system on 19 stroke survivors to estimate the Functional Ability Scale (FAS) of reaching movements. The analysis showed that we can estimate the FAS scores of reaching movements, with some confusion between adjacent scores. Furthermore, we estimated the average FAS scores of subjects with a normalized root mean square error (NRMSE) of 22.5%. Though our model's high error on two severe subjects influenced our overall estimation performance, we could accurately estimate scores in most of the mild-to-moderate subjects (NRMSE of 13.1% without the outliers). With further development and testing, we believe the proposed technique can be applied to monitor patient recovery in home and community settings.Early diagnosis of mild traumatic brain injury (mTBI) is challenging, yet significantly important in order to grant the patients with timely treatment and mitigating the risks of possible long-term psychiatric and neurological disorders. To tackle this problem, in this paper, we develop an mTBI detection framework based on graph embedding features combined with convolutional neural networks (CNN). Cortical activity in transgenic calcium reporter mice expressing Thy1-GCaMP6s is recorded in two sessions, prior to and after inducing injury. Functional networks are then constructed for recordings obtained in each session. The Node2vec algorithm is employed to represent nodes of these networks in the node embedding space. Node embedding feature vectors are then aligned, compressed, and represented as three-channel images. A CNN model is used for the classification of brain networks into two categories of normal and mTBI. A maximum classification accuracy of 95.4% is achieved. Our results suggest that functional networks as biomarkers along with the proposed method can effectively be used for detecting mTBI.One crucial key of developing an automatic sleep stage scoring method is to extract discriminative features. In this paper, we present a novel technique, termed common frequency pattern (CFP), to extract the variance features from a single-channel electroencephalogram (EEG) signal for sleep stage classification. The learning task is formulated by finding significant frequency patterns that maximize variance for one class and that at the same time, minimize variance for the other class. The proposed methodology for automated sleep scoring is tested on the benchmark Sleep-EDF database and finally achieves 97.9%, 94.22%, and 90.16% accuracy for two-state, three-state, and five-state classification of sleep stages. Experimental results demonstrate that the proposed method identifies discriminative characteristics of sleep stages robustly and achieves better performance as compared to the state-of-the-art sleep staging algorithms. Apart from the enhanced classification, the frequency patterns that are determined by the CFP algorithm is able to find the most significant bands of frequency for classification and could be helpful for a better understanding of the mechanisms of sleep stages.