Multi-dimensional hybrid bilinear CNN-LSTM models for epileptic seizure detection and prediction using EEG signals.

Objective: Automatic detection and prediction of epilepsy are crucial for improving patient care and quality of life. However, existing methods typically focus on single-dimensional information and often confuse the periodic and aperiodic components in electrophysiological signals.

Approach: We propose a novel deep learning framework that integrates temporal, spatial, and frequency information of EEG signals, in which periodic and aperiodic components are separated in the frequency domain.

Can Transcutaneous Laryngeal Ultrasonography Systematically Quantify Vocal Fold Movement?-A Feasibility Study.

Objectives: Several studies have assessed adult vocal fold movement using transcutaneous laryngeal ultrasonography (TLUSG) during the perioperative period of thyroidectomy. However, the movement was not objectively quantified. This study aimed to provide a feasible and objective method for assessing vocal fold movement using TLUSG.

Epileptic Seizure Detection and Prediction in EEGs Using Power Spectra Density Parameterization.

Power spectrum analysis is one of the effective tools for classifying epileptic signals based on electroencephalography (EEG) recordings. However, the conflation of periodic and aperiodic components within the EEG may presents an obstacle to epilepsy detection or prediction. In this paper, we explored the significance of the periodic and aperiodic components of the EEG power spectrum for the detection and prediction of epilepsy respectively.

Aberrant temporal correlations of ongoing oscillations in disorders of consciousness on multiple time scales.

Unlabelled: Changes in neural oscillation amplitude across states of consciousness has been widely reported, but little is known about the link between temporal dynamics of these oscillations on different time scales and consciousness levels. To address this question, we analyzed amplitude fluctuation of the oscillations extracted from spontaneous resting-state EEG recorded from the patients with disorders of consciousness (DOC) and healthy controls. Detrended fluctuation analysis (DFA) and measures of life-time and waiting-time were employed to characterize the temporal structure of EEG oscillations on long time scales (1-20 s) and short time scales (< 1 s), in groups with different consciousness states: patients in minimally conscious state (MCS), patients with unresponsive wakefulness syndrome (UWS) and healthy subjects.

Effects of age, sex, and education on California Verbal Learning Test-II performance in a Chinese-speaking population.

The California Verbal Learning Test-Second Edition (CVLT-II), is a commonly used tool to assess episodic memory. This study analyzed learning and memory characteristics in a cognitively healthy Chinese population, as well as the effects of age, sex and education on CVLT-II factors. In total, 246 healthy people aged 20-80 years and 29 persons with multiple sclerosis (MS) were included in this study and completed the CVLT-II.

Disrupted Control Architecture of Brain Network in Disorder of Consciousness.

The human brain controls various cognitive functions via the functional coordination of multiple brain regions in an efficient and robust way. However, the relationship between consciousness state and the control mode of brain networks is poorly explored. Using multi-channel EEG, the present study aimed to characterize the abnormal control architecture of functional brain networks in the patients with disorders of consciousness (DOC).

Application of an ultrasound semi-quantitative assessment in the degradation of silk fibroin scaffolds in vivo.

Background: Research on the degradation of silk fibroin (SF) scaffolds in vivo lacks uniform and effective standards and experimental evaluation methods. This study aims to evaluate the application of ultrasound in assessing the degradation of SF scaffolds.

Methods: Two groups of three-dimensional regenerated SF scaffolds (3D RSFs) were implanted subcutaneously into the backs of Sprague-Dawley rats.

Identification of Alzheimer's EEG With a WVG Network-Based Fuzzy Learning Approach.

A novel analytical framework combined fuzzy learning and complex network approaches is proposed for the identification of Alzheimer's disease (AD) with multichannel scalp-recorded electroencephalograph (EEG) signals. Weighted visibility graph (WVG) algorithm is first applied to transform each channel EEG into network and its topological parameters were further extracted. Statistical analysis indicates that AD and normal subjects show significant difference in the structure of WVG network and thus can be used to identify Alzheimer's disease.

Altered inter-frequency dynamics of brain networks in disorder of consciousness.

Objective: Growing evidence have linked disorders of consciousness (DOC) with the changes in frequency-specific functional networks. However, the alteration of inter-frequency dynamics in brain networks remain largely unknown. In this study, we investigated the network integration and segregation across frequency bands in a multiplex network framework.

Functional Integration and Segregation in Multiplex Brain Networks for Alzheimer's Disease.

Growing evidence links impairment of brain functions in Alzheimer's disease (AD) with disruptions of brain functional connectivity. However, whether the AD brain shows similar changes from a dynamic or cross-frequency view remains poorly explored. This paper provides an effective framework to investigate the properties of multiplex brain networks in AD considering inter-frequency and temporal dynamics.

Characterization of network switching in disorder of consciousness at multiple time scales.

Objective: Recent works have shown that flexible information processing is closely related to the reconfiguration of human brain networks underlying brain functions. However, the role of network switching for consciousness is poorly explored and whether such transition can indicate the behavioral performance of patients with disorders of consciousness (DOC) remains unknown. Here, we investigate the relationship between the switching of brain networks (states) over time and the consciousness levels.

Variation of functional brain connectivity in epileptic seizures: an EEG analysis with cross-frequency phase synchronization.

Frequency coupling in nervous system is believed to be associated with normal and impaired brain functions. However, most of the existing experiments have been concentrated on the coupling strength within frequency bands, while the coupling strength between different bands is ignored. In this work, we apply phase synchronization index (PSI) to investigate the cross-frequency coupling (CFC) of Electroencephalogram (EEG) signals.

Spatiotemporal EEG microstate analysis in drug-free patients with Parkinson's disease.

The clinical diagnosis of Parkinson's disease (PD) is very difficult, especially in the early stage of the disease, because there is no physiological indicator that can be referenced. Drug-free patients with early PD are characterized by clinical symptoms such as impaired motor function and cognitive decline, which was caused by the dysfunction of brain's dynamic activities. The indicators of brain dysfunction in patients with PD at an early unmedicated condition may provide a valuable basis for the diagnosis of early PD and later treatment.

Analysis of Spontaneous EEG Activity in Alzheimer's Disease Using Weighted Visibility Graph.

This study was aimed at characterizing spontaneous electroencephalography (EEG) activity in Alzheimer's disease (AD) using a novel approach named weighted visibility graph (WVG). More than 10 minutes of spontaneous EEG were recorded from 15 AD patients and 15 age-matched normal controls. Two graph metrics, clustering coefficient and average weighted degree, are extracted in different frequency bands for each EEG channel based on the WVG methodology.

Modulation of Spectral Power and Functional Connectivity in Human Brain by Acupuncture Stimulation.

Acupuncture, as an external stimulation, can produce clinical effects via the central nervous system. In order to investigate the modulatory efficacy of acupuncture on brain activity, multichannel EEG signals evoked by acupuncture at "Zusanli" acupoint were recorded from healthy humans in three states: pre-acupuncture, acupuncture, and post-acupuncture. Power spectral density is first used to analyze the EEG power change during acupuncture process.

Gamma rhythm low field magnetic stimulation alleviates neuropathologic changes and rescues memory and cognitive impairments in a mouse model of Alzheimer's disease.

Introduction: The abnormal amyloid β (Aβ) accumulation and Aβ-related neural network dysfunction are considered central to the pathogenesis of Alzheimer's disease (AD) at the early stage. Deep-brain reachable low field magnetic stimulation (DMS), a novel noninvasive approach that was designed to intervene the network activity in brains, has been found to alleviate stress-related cognitive impairments.

Methods: Amyloid precursor protein/presenilin-1 transgenic mice (5XFAD) were treated with DMS, and cognitive behavior and AD-like pathologic changes in the neurochemical and electrophysiological properties in 5XFAD mice were assessed.

Multivariate multi-scale weighted permutation entropy analysis of EEG complexity for Alzheimer's disease.

The complexity change of brain activity in Alzheimer's disease (AD) is an interesting topic for clinical purpose. To investigate the dynamical complexity of brain activity in AD, a multivariate multi-scale weighted permutation entropy (MMSWPE) method is proposed to measure the complexity of electroencephalograph (EEG) obtained in AD patients. MMSWPE combines the weighted permutation entropy and the multivariate multi-scale method.

The Intervention of CRAC Channels Alleviates Inflammatory Responses in Nasal Polyps.

Background: Chronic rhinosinusitis with nasal polyps (CRSwNP) is associated with Th2-dominant inflammation. However, effective treatments for CRSwNP have not yet been found. This study aimed to investigate the expression of Orai1 in nasal polyps (NP) and the influence on them of the intervention of Ca2+ release-activated Ca2+ (CRAC) channels.

Characterization of complexity in the electroencephalograph activity of Alzheimer's disease based on fuzzy entropy.

In this paper, experimental neurophysiologic recording and statistical analysis are combined to investigate the nonlinear characteristic and the cognitive function of the brain. Fuzzy approximate entropy and fuzzy sample entropy are applied to characterize the model-based simulated series and electroencephalograph (EEG) series of Alzheimer's disease (AD). The effectiveness and advantages of these two kinds of fuzzy entropy are first verified through the simulated EEG series generated by the alpha rhythm model, including stronger relative consistency and robustness.

In Vitro Treatment with 2-APB Inhibits the Inflammation in Nasal Polyps.

Objective: Glucocorticoids are considered the main treatment option for chronic rhinosinusitis with nasal polyps (CRSwNP), but their effect rate ranges from 60.9% to 80%. Novel therapeutic means should be studied.

[Expression of S518 phosphorylated Merlin and its interaction with CD44 in vestibular schwannoma].

Objective: To investigate the impact of S518 phosphorylation in Merlin on the interaction with CD44 in vestibular schwannoma and the tumor growth.

Methods: Thirty-five samples of vestibular schwannoma were identified by pathology. Immunohistopathology and western blot were employed to analyze the expression and localization of S518 phosphorylated Merlin in the tumor tissues.

Compensative shuttling of merlin to phosphorylation on serine 518 in vestibular schwannoma.

Background: Vestibular schwannoma (VS) is a benign tumor with malignant biological consequence because of its special location, and its occurrence is highly related to merlin. Cell culture studies have demonstrated that merlin acts as a molecular linker between the cytoskeleton and specific membrane proteins and is linked to cell cycle control. Therefore, we sought to detect the expression of endogenous merlin and its subcellular distribution using cyclin D1 as a cell cycle marker in VS.