Drejerhicks5371

Fugl-Meyer assessment is an accepted method of evaluating motor function for people with stroke. A challenge associated with this assessment is the availability of trained examiners to carry out the evaluation. Neurophysiological biomarkers show promise in addressing the above impediment. Our study investigated the potential of using resting state electroencephalographic (EEG) functional connectivity measures as biomarkers for estimating Fugl-Meyer upper extremity motor score (FMU) in people with chronic stroke. Resting state EEG was recorded from 10 individuals with stroke. Functional connectivity was evaluated through five different processing algorithms and quantified in terms of maximum-coherence between EEG electrodes at 15 frequencies from 1 to 45 Hz. We applied a multi-variate Partial Least Squares (PLS) Correlation analysis to simultaneously identify specific connectivity channels (EEG electrode pairings) and frequencies that robustly correlated with FMU. We then applied PLS-Regression to the identified channels and frequencies to generate a set of coefficients for estimating the FMU. Participants were randomly assigned to a training-set of eight and a test-set of two. Crossvalidation with leave-one-out approach on the training-set, using Phase-Lag-Index processing algorithm, resulted in an R2 of 0.97 and a least-square linear fit slope of 1 for predicted versus actual FMU, with a root-mean-square error of 1.9 on FMU scale. Application of regression coefficients to the connectivity measures from the test-set resulted in predicted FMU of 47 and 38 versus actual scores of 46 and 39, respectively. Our results demonstrated that the evaluation of neural correlates of FMU shows promise in addressing the challenges associated with the availability of trained examiners to carry out the assessments.Functional assessment is an essential part of rehabilitation protocols after stroke. Conventionally, the assessment process relies heavily on clinical experience and lacks quantitative analysis. In order to objectively quantify the upper-limb motor impairments in patients with post-stroke hemiparesis, this study proposes a novel assessment approach based on motor synergy quantification and multi-modality fusion. Fifteen post-stroke hemiparetic patients and fifteen age-matched healthy persons participated in this study. During different goal-directed tasks, kinematic data and surface electromyography (sEMG) signals were synchronously collected from these participants, and then motor features extracted from each modal data can be fed into the respective local classifiers. In addition, kinematic synergies and muscle synergies were quantified by principal component analysis (PCA) and k weighted angular similarity (kWAS) algorithm to provide in-depth analysis of the coactivated features responsible for observable d a significant correlation with the score of standard clinical tests (R= -0.87, P=1.98e-5). These promising results show the feasibility of applying the proposed method to clinical assessments for post-stroke hemiparetic patients.Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive brain stimulation technique that can influence cortical excitability. Low-frequency rTMS (stimulation frequency.1Hz) can induce inhibitory effects on cortical excitability. In order to investigate dynamic changes in neuronal activity after low-frequency rTMS, 20 healthy subjects received 1-Hz rTMS over the right motor area, and electroencephalography (EEG) in resting condition with eyes open was recorded before rTMS and at 0 min, 20 min, 40 min, and 60 min after rTMS. Power values, functional connectivity based on a weighted phase lag index (wPLI), and network characteristics were assessed and compared to study the aftereffects of rTMS. Our results show that low-frequency rTMS produced a delayed long-lasting increase in alpha-band power values in frontoparietal brain areas and an immediate long-lasting increase in theta-band power values in the ipsilateral frontal and contralateral centroparietal areas. In the alpha band, functional connectivity decreased immediately after rTMS but significantly increased at 20 min after rTMS. Moreover, an analysis of undirected graphs revealed that the number of connections significantly changed in the anterior and posterior regions in the alpha band. In addition, there were significant decreases in clustering coefficients of the channels near the site of stimulation in the alpha and theta bands after rTMS. In conclusion, low-frequency rTMS produces widespread and long-lasting alterations in neural oscillation and functional connectivity. This work implies that low-frequency rTMS can induce inhibitory effects on motor cortical excitability ipsilateral to the stimulation site.Blind and universal image denoising consists of using a unique model that denoises images with any level of noise. It is especially practical as noise levels do not need to be known when the model is developed or at test time. We propose a theoretically-grounded blind and universal deep learning image denoiser for additive Gaussian noise removal. Our network is based on an optimal denoising solution, which we call fusion denoising. It is derived theoretically with a Gaussian image prior assumption. Synthetic experiments show our network's generalization strength to unseen additive noise levels. We also adapt the fusion denoising network architecture for image denoising on real images. PHA-767491 purchase Our approach improves real-world grayscale additive image denoising PSNR results for training noise levels and further on noise levels not seen during training. It also improves state-of-the-art color image denoising performance on every single noise level, by an average of 0.1dB, whether trained on or not.RGB-D based salient object detection (SOD) methods leverage the depth map as a valuable complementary information for better SOD performance. Previous methods mainly resort to exploit the correlation between RGB image and depth map in three fusion domains input images, extracted features, and output results. However, these fusion strategies cannot fully capture the complex correlation between the RGB image and depth map. Besides, these methods do not fully explore the cross-modal complementarity and the cross-level continuity of information, and treat information from different sources without discrimination. In this paper, to address these problems, we propose a novel Information Conversion Network (ICNet) for RGB-D based SOD by employing the siamese structure with encoder-decoder architecture. To fuse high-level RGB and depth features in an interactive and adaptive way, we propose a novel Information Conversion Module (ICM), which contains concatenation operations and correlation layers. Furthermore, we design a Cross-modal Depth-weighted Combination (CDC) block to discriminate the cross-modal features from different sources and to enhance RGB features with depth features at each level. Extensive experiments on five commonly tested datasets demonstrate the superiority of our ICNet over 15 state-of-theart RGB-D based SOD methods, and validate the effectiveness of the proposed ICM and CDC block.Block transform coded images usually suffer from annoying artifacts at low bit-rates, because of the independent quantization of DCT coefficients. Image prior models play an important role in compressed image reconstruction. Natural image patches in a small neighborhood of the high-dimensional image space usually exhibit an underlying sub-manifold structure. To model the distribution of signal, we extract sub-manifold structure as prior knowledge. We utilize graph Laplacian regularization to characterize the sub-manifold structure at patch level. And similar patches are exploited as samples to estimate distribution of a particular patch. Instead of using Euclidean distance as similarity metric, we propose to use graph-domain distance to measure the patch similarity. Then we perform low-rank regularization on the similar-patch group, and incorporate a non-convex lp penalty to surrogate matrix rank. Finally, an alternatively minimizing strategy is employed to solve the non-convex problem. Experimental results show that our proposed method is capable of achieving more accurate reconstruction than the state-of-the-art methods in both objective and perceptual qualities.In contrast with nature scenes, aerial scenes are often composed of many objects crowdedly distributed on the surface in bird's view, the description of which usually demands more discriminative features as well as local semantics. However, when applied to scene classification, most of the existing convolution neural networks (ConvNets) tend to depict global semantics of images, and the loss of low- and mid-level features can hardly be avoided, especially when the model goes deeper. To tackle these challenges, in this paper, we propose a multiple-instance densely-connected ConvNet (MIDC-Net) for aerial scene classification. It regards aerial scene classification as a multiple-instance learning problem so that local semantics can be further investigated. Our classification model consists of an instance-level classifier, a multiple instance pooling and followed by a bag-level classification layer. In the instance-level classifier, we propose a simplified dense connection structure to effectively preserve features from different levels. The extracted convolution features are further converted into instance feature vectors. Then, we propose a trainable attention-based multiple instance pooling. It highlights the local semantics relevant to the scene label and outputs the bag-level probability directly. Finally, with our bag-level classification layer, this multiple instance learning framework is under the direct supervision of bag labels. Experiments on three widely-utilized aerial scene benchmarks demonstrate that our proposed method outperforms many state-of-the-art methods by a large margin with much fewer parameters.Shear wave speed measurements can potentially be used to non-invasively measure myocardial stiffness in order to assess myocardial function. Several studies showed the feasibility of tracking natural mechanical waves induced by aortic valve closure in the interventricular septum, but different echocardiographic views have been used. This work systematically studied the wave propagation speeds measured in a parasternal long-axis and in an apical 4-chamber view in ten healthy volunteers. The apical and parasternal view are predominantly sensitive to longitudinal or transversal tissue motion respectively, and could therefore, theoretically, measure the speed of different wave modes. We found higher propagation speeds in apical than in parasternal view (median of 5.1 m/s vs 3.8 m/s, p less then 0.01, n=9). The results in the different views were not correlated (r=0.26, p=0.49), and an unexpectedly large variability among healthy volunteers was found in apical view compared to the parasternal view (3.5 - 8.7 vs 3.2 - 4.3 m/s, respectively). Complementary finite element simulations of Lamb waves in an elastic plate showed that different propagation speeds can be measured for different particle motion components when different wave modes are induced simultaneously. The in-vivo results cannot be fully explained with the theory of Lamb wave modes. Nonetheless, the results suggest that the parasternal long-axis view is a more suitable candidate for clinical diagnosis due to the lower variability in wave speeds.