Changes in cerebral autoregulation, stroke-related blood biomarkers, and autonomic regulation after patent foramen ovale closure in severe migraine patients.

Aims: This study aimed to investigate changes in dynamic cerebral autoregulation (dCA), 20 stroke-related blood biomarkers, and autonomic regulation after patent foramen ovale (PFO) closure in severe migraine patients.

Methods: Patent foramen ovale severe migraine patients, matched non-PFO severe migraine patients, and healthy controls were included. dCA and autonomic regulation were evaluated in each participant at baseline, and within 48-h and 30 days after closure in PFO migraineurs.

Spatio-Temporal-Spectral Hierarchical Graph Convolutional Network With Semisupervised Active Learning for Patient-Specific Seizure Prediction.

Graph theory analysis using electroencephalogram (EEG) signals is currently an advanced technique for seizure prediction. Recent deep learning approaches, which fail to fully explore both the characterizations in EEGs themselves and correlations among different electrodes simultaneously, generally neglect the spatial or temporal dependencies in an epileptic brain and, thus, produce suboptimal seizure prediction performance consequently. To tackle this issue, in this article, a patient-specific EEG seizure predictor is proposed by using a novel spatio-temporal-spectral hierarchical graph convolutional network with an active preictal interval learning scheme (STS-HGCN-AL).

Epileptic Seizure Detection in EEG Signals Using a Unified Temporal-Spectral Squeeze-and-Excitation Network.

The intelligent recognition of epileptic electro-encephalogram (EEG) signals is a valuable tool for the epileptic seizure detection. Recent deep learning models fail to fully consider both spectral and temporal domain representations simultaneously, which may lead to omitting the nonstationary or nonlinear property in epileptic EEGs and further produce a suboptimal recognition performance consequently. In this paper, an end-to-end EEG seizure detection framework is proposed by using a novel channel-embedding spectral-temporal squeeze-and-excitation network (CE-stSENet) with a maximum mean discrepancy-based information maximizing loss.

A Channel-Projection Mixed-Scale Convolutional Neural Network for Motor Imagery EEG Decoding.

Motor imagery electroencephalography (EEG) decoding is an essential part of brain-computer interfaces (BCIs) which help motor-disabled patients to communicate with the outside world by external devices. Recently, deep learning algorithms using decomposed spectrums of EEG as inputs may omit important spatial dependencies and different temporal scale information, thus generated the poor decoding performance. In this paper, we propose an end-to-end EEG decoding framework, which employs raw multi-channel EEG as inputs, to boost decoding accuracy by the channel-projection mixed-scale convolutional neural network (CP-MixedNet) aided by amplitude-perturbation data augmentation.

Transcriptomic analysis reveals key lncRNAs associated with ribosomal biogenesis and epidermis differentiation in head and neck squamous cell carcinoma.

Objective: In this study, we aimed to expand current knowledge of head and neck squamous cell carcinoma (HNSCC)-associated long noncoding RNAs (lncRNAs), and to discover potential lncRNA prognostic biomarkers for HNSCC based on next-generation RNA-seq.

Methods: RNA-seq data of 546 samples from patients with HNSCC were downloaded from The Cancer Genome Atlas (TCGA), including 43 paired samples of tumor tissue and adjacent normal tissue. An integrated analysis incorporating differential expression, weighted gene co-expression networks, functional enrichment, clinical parameters, and survival analysis was conducted to discover HNSCC-associated lncRNAs.

Time-Varying System Identification Using an Ultra-Orthogonal Forward Regression and Multiwavelet Basis Functions With Applications to EEG.

A new parametric approach is proposed for nonlinear and nonstationary system identification based on a time-varying nonlinear autoregressive with exogenous input (TV-NARX) model. The TV coefficients of the TV-NARX model are expanded using multiwavelet basis functions, and the model is thus transformed into a time-invariant regression problem. An ultra-orthogonal forward regression (UOFR) algorithm aided by mutual information (MI) is designed to identify a parsimonious model structure and estimate the associated model parameters.

Effectiveness and Safety of Transcatheter Patent Foramen Ovale Closure for Migraine (EASTFORM) Trial.

We evaluated the safety and effectiveness of transcatheter patent foramen ovale (PFO) closure for the treatment of migraine in a Chinese population. This non-randomized clinical trial enrolled 258 consecutive substantial or severe migraineurs with a right-to-left shunt (RLS) (grade II-IV) and grouped subjects according to their election or refusal of PFO closure. Migraine was diagnosed according to the International Classification of Headache Disorders III-beta and evaluated using the Headache Impact Test-6 (HIT-6).

Comparison of Vertebral Artery and Middle Cerebral Artery Monitoring for Right-to-left Shunt Detection by Contrast-enhanced Transcranial Doppler.

Contrast-enhanced transcranial Doppler (c-TCD) is a reliable and reproducible method for right-to-left shunt (RLS) detection, with high sensitivity. Monitoring the middle cerebral artery (MCA) is an optimal choice, yet for patients with insufficient temporal bone windows or severe stenosis of carotid arteries, an alternative should be established. The aim of the present study was to further establish whether c-TCD with vertebral artery (VA) monitoring is as effective as MCA monitoring for RLS detection.

Comparison of Different Methods of Valsalva Maneuver for Right-to-left Shunt Detection by Contrast-Enhanced Transcranial Doppler.

We evaluated 298 patients for right-to-left shunt (RLS) detection by contrast-enhanced transcranial Doppler at rest state (RS), during the conventional Valsalva maneuver (CM), and during the modified Valsalva maneuver (BM: blowing into the connecting tube of a sphygmomanometer at 40 mm Hg for 10 s) in random order, and the degree of RLS along the time of the first microbubble occurrence was recorded. The positive rates were 21.8%, 36.