Purpose: Spatial neglect is common after right-hemisphere stroke and has proven resilient to a number of therapeutic interventions. Both active and experimenter-induced passive movements of the left limb in left hemispace have been shown to ameliorate neglect in subsets of patients by improving performance on tasks requiring attention to the left side of space. However, the high incidence of contralesional hemiparesis and poor motor recovery in neglect makes active limb movement therapies applicable to only a small subset of patients. The purpose of our studies was to investigate the effects of passive movements of the left hand by functional electrical stimulation (FES), a common and portable motor rehabilitation technique, on performance in a visual scanning task.
Methods: The effect of FES-induced passive movement on target detection in a visual scanning task was compared to no movement and active movement conditions and also investigated in scanning tasks in both near and far space.
Results: Passive limb movement effects in neglect were variable across and within studies, reference spaces, and individuals, with a subset of positive responders differing from non-responders in regard to constructional deficits and lesion location.
Conclusions: The potential viability of FES as a therapy for neglect deserves further investigation and directions for future research in this area are discussed.
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Med Phys
January 2025
School of Computer Science and Engineering, Beihang University, Beijing, China.
Background: Computed tomography angiography (CTA) is used to screen for coronary artery calcification. As the coronary artery has complicated structure and tiny lumen, manual screening is a time-consuming task. Recently, many deep learning methods have been proposed for the segmentation (SEG) of coronary artery and calcification, however, they often neglect leveraging related anatomical prior knowledge, resulting in low accuracy and instability.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region, China.
Deep learning models have shown promise in diagnosing neurodevelopmental disorders (NDD) like ASD and ADHD. However, many models either use graph neural networks (GNN) to construct single-level brain functional networks (BFNs) or employ spatial convolution filtering for local information extraction from rs-fMRI data, often neglecting high-order features crucial for NDD classification. We introduce a Multi-view High-order Network (MHNet) to capture hierarchical and high-order features from multi-view BFNs derived from rs-fMRI data for NDD prediction.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
Centre for Big Data Research in Health, University of New South Wales, Sydney, Australia.
Background: Due to the complexity and cost of preparing histopathological slides, deep learning-based methods have been developed to generate high-quality histological images. However, existing approaches primarily focus on spatial domain information, neglecting the periodic information in the frequency domain and the complementary relationship between the two domains. In this paper, we proposed a generative adversarial network that employs a cross-attention mechanism to extract and fuse features across spatial and frequency domains.
View Article and Find Full Text PDFExp Brain Res
January 2025
Center of Neurology, Division of Neuropsychology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.
Vibrating muscles to manipulate proprioceptive input creates the sensation of an apparent change in body position. This study investigates whether vibrating the right biceps muscle has similar effects as vibrating the left posterior neck muscles. Based on previous observations, we hypothesized that both types of muscle vibration would shift the perception of healthy subjects' subjective straight-ahead (SSA) orientation in the horizontal plane to the left.
View Article and Find Full Text PDFSensors (Basel)
January 2025
College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Gesture recognition technology based on millimeter-wave radar can recognize and classify user gestures in non-contact scenarios. To address the complexity of data processing with multi-feature inputs in neural networks and the poor recognition performance with single-feature inputs, this paper proposes a gesture recognition algorithm based on esNet ong Short-Term Memory with an ttention Mechanism (RLA). In the aspect of signal processing in RLA, a range-Doppler map is obtained through the extraction of the range and velocity features in the original mmWave radar signal.
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