Symmetrical analysis of paravertebral muscles during superman exercise in patients with adolescent idiopathic scoliosis.

Background: To investigate the asymmetry of paravertebral muscles during the superman exercise in patients with adolescent idiopathic scoliosis, and to analyze the applicability of this exercise for the rehabilitation of these patients from the perspective of muscle electromyographic activity.

Methods: 37 patients with adolescent idiopathic scoliosis are selected for this study. Surface electromyography signals of the bilateral paravertebral muscles during the daily sitting and the superman exercise are recorded.

An Auxiliary Synthesis Framework for Enhancing EEG-Based Classification With Limited Data.

While deep learning algorithms significantly improves the decoding performance of brain-computer interface (BCI) based on electroencephalogram (EEG) signals, the performance relies on a large number of high-resolution data for training. However, collecting sufficient usable EEG data is difficult due to the heavy burden on the subjects and the high experimental cost. To overcome this data insufficiency, a novel auxiliary synthesis framework is first introduced in this paper, which composes of a pre-trained auxiliary decoding model and a generative model.

Neural excitatory rebound induced by valproic acid may predict its inadequate control of seizures.

Background: Valproic acid (VPA) represents one of the most efficient antiseizure medications (ASMs) for both general and focal seizures, but some patients may have inadequate control by VPA monotherapy. In this study, we aimed to verify the hypothesis that excitatory dynamic rebound induced by inhibitory power may contribute to the ineffectiveness of VPA therapy and become a predictor of post-operative inadequate control of seizures.

Methods: Awake craniotomy surgeries were performed in 16 patients with intro-operative high-density electrocorticogram (ECoG) recording.

EEG-Based Driving Fatigue Detection Using a Two-Level Learning Hierarchy Radial Basis Function.

Electroencephalography (EEG)-based driving fatigue detection has gained increasing attention recently due to the non-invasive, low-cost, and potable nature of the EEG technology, but it is still challenging to extract informative features from noisy EEG signals for driving fatigue detection. Radial basis function (RBF) neural network has drawn lots of attention as a promising classifier due to its linear-in-the-parameters network structure, strong non-linear approximation ability, and desired generalization property. The RBF network performance heavily relies on network parameters such as the number of the hidden nodes, number of the center vectors, width, and output weights.

An epidermal sEMG tattoo-like patch as a new human-machine interface for patients with loss of voice.

Throat cancer treatment involves surgical removal of the tumor, leaving patients with facial disfigurement as well as temporary or permanent loss of voice. Surface electromyography (sEMG) generated from the jaw contains lots of voice information. However, it is difficult to record because of not only the weakness of the signals but also the steep skin curvature.

A Multi-Branch 3D Convolutional Neural Network for EEG-Based Motor Imagery Classification.

One of the challenges in motor imagery (MI) classification tasks is finding an easy-handled electroencephalogram (EEG) representation method which can preserve not only temporal features but also spatial ones. To fully utilize the features on various dimensions of EEG, a novel MI classification framework is first introduced in this paper, including a new 3D representation of EEG, a multi-branch 3D convolutional neural network (3D CNN) and the corresponding classification strategy. The 3D representation is generated by transforming EEG signals into a sequence of 2D array which preserves spatial distribution of sampling electrodes.

A modified fuzzy C-means method for segmenting MR images using non-local information.

Background: In recent years, MR images have been increasingly used in therapeutic applications such as image-guided radiotherapy (IGRT). However, images with low contrast values and noises present challenges for image segmentation.

Objective: The objective of this study is to develop a robust method based on fuzzy C-means (FCM) method which can segment MR images polluted with Gaussian noise.

Scutellarin Alleviates Behavioral Deficits in a Mouse Model of Multiple Sclerosis, Possibly Through Protecting Neural Stem Cells.

Scutellarin, a flavonoid extracted from an herbal medication (Erigeron breviscapus Hand-Mazz), has been shown to protect neurons against damage and to promote neurogenesis, and thus has therapeutic potential in the treatment of a variety of neurodegenerative diseases. Since neural stem cells (NSCs) could differentiate into myelin-producing oligodendrocytes, we speculate that scutellarin could also be used to treat multiple sclerosis (MS). In the current study, we examined potential effects of scutellarin using a mouse model of MS.

Optimization of large-scale mouse brain connectome via joint evaluation of DTI and neuron tracing data.

Tractography based on diffusion tensor imaging (DTI) data has been used as a tool by a large number of recent studies to investigate structural connectome. Despite its great success in offering unique 3D neuroanatomy information, DTI is an indirect observation with limited resolution and accuracy and its reliability is still unclear. Thus, it is essential to answer this fundamental question: how reliable is DTI tractography in constructing large-scale connectome? To answer this question, we employed neuron tracing data of 1772 experiments on the mouse brain released by the Allen Mouse Brain Connectivity Atlas (AMCA) as the ground-truth to assess the performance of DTI tractography in inferring white matter fiber pathways and inter-regional connections.

Construct and assess multimodal mouse brain connectomes via joint modeling of multi-scale DTI and neuron tracer data.

Mapping the neuronal wiring diagrams in the brain at multiple spatial scales has been one of the major brain mapping objectives. Macro-scale medical imaging modalities such as diffusion tensor imaging (DTI) and meso-scale biological imaging such as serial two-photon tomography have emerged as the prominent tools to reveal structural connectivity patterns at multiple scales. However, a significant gap that whether/how DTI data and microscopic data are correlated with each other for the s ame species of mammalian brains,e.

Mapping and deciphering neural codes of NMDA receptor-dependent fear memory engrams in the hippocampus.

Mapping and decoding brain activity patterns underlying learning and memory represents both great interest and immense challenge. At present, very little is known regarding many of the very basic questions regarding the neural codes of memory: are fear memories retrieved during the freezing state or non-freezing state of the animals? How do individual memory traces give arise to a holistic, real-time associative memory engram? How are memory codes regulated by synaptic plasticity? Here, by applying high-density electrode arrays and dimensionality-reduction decoding algorithms, we investigate hippocampal CA1 activity patterns of trace fear conditioning memory code in inducible NMDA receptor knockout mice and their control littermates. Our analyses showed that the conditioned tone (CS) and unconditioned foot-shock (US) can evoke hippocampal ensemble responses in control and mutant mice.

Predicting perceptual suppression from local field potential in visual cortex.

Generalized flash suppression (GFS, Wilke et al. 2003) in which a salient visual stimulus can be rendered invisible despite continuous retinal input has provided a powerful means to study neural processes directly related to perception. However, the mechanisms underlying such perceptual suppression remain poorly understood.

Factors influencing clinical treatment for severe chronic periodontitis.

Electronic patient records (EPR) may facilitate further articulation, quantification and improvement of the parameter on chronic periodontitis and periodontal treatment decisions.

Measurement of bioelectric currents based on the coupling of electric and magnetic field.

A method based on magneto-acoustic technique is developed to measure bioelectric currents non-invasively. Theoretically, vibrations of particles in the medium are produced due to the Lorentz force when oscillatory currents flow through medium in a steady magnetic field. These vibrations contain information of the bioelectric currents and can be detected by surface contact detector.