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. A panel of stroke-related blood biomarkers was detected pre-surgically in arterial-and venous blood, and post-surgically in the arterial blood in PFO migraineurs. RESULTS: Forty-five PFO severe migraine patients, 50 non-PFO severe migraine patients, and 50 controls were enrolled. The baseline dCA function of PFO migraineurs was significantly lower than that of non-PFO migraineurs and controls but was rapidly improved with PFO closure, remaining stable at 1-month follow-up. Arterial blood platelet-derived growth factor-BB (PDGF-BB) levels were higher in PFO migraineurs than in controls, which was immediately and significantly reduced after closure. No differences in autonomic regulation were observed among the three groups. CONCLUSION: Patent foramen ovale closure can improve dCA and alter elevated arterial PDGF-BB levels in migraine patients with PFO, both of which may be related to the preventive effect of PFO closure on stroke occurrence/recurrence.

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). Specifically, since the epileptic activities in different brain regions may be of different frequencies, the proposed STS-HGCN-AL framework first infers a hierarchical graph to concurrently characterize an epileptic cortex under different rhythms, whose temporal dependencies and spatial couplings are extracted by a spectral-temporal convolutional neural network and a variant self-gating mechanism, respectively. Critical intrarhythm spatiotemporal properties are then captured and integrated jointly and further mapped to the final recognition results by using a hierarchical graph convolutional network. Particularly, since the preictal transition may be diverse from seconds to hours prior to a seizure onset among different patients, our STS-HGCN-AL scheme estimates an optimal preictal interval patient dependently via a semisupervised active learning strategy, which further enhances the robustness of the proposed patient-specific EEG seizure predictor. Competitive experimental results validate the efficacy of the proposed method in extracting critical preictal biomarkers, indicating its promising abilities in automatic seizure prediction.

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. Specifically, the CE-stSENet firstly integrates both multi-level spectral and multi-scale temporal analysis simultaneously. Hierarchical multi-domain representations are then captured in a unified manner with a variant of squeeze-and-excitation block. The classification net is finally implemented for epileptic EEG recognition based on features extracted in previous subnetworks. Particularly, to address the fact that the scarcity of seizure events results in finite data distribution and the severe overfitting problem in seizure detection, the CE-stSENet is coordinated with a maximum mean discrepancy-based information maximizing loss for mitigating the overfitting problem. Competitive experimental results on three EEG datasets against the state-of-the-art methods demonstrate the effectiveness of the proposed framework in recognizing epileptic EEGs, indicating its powerful capability in the automatic seizure detection.

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. Specifically, the first block in CP-MixedNet is designed to learn primary spatial and temporal representations from EEG signals. The mixed-scale convolutional block is then used to capture mixed-scale temporal information, which effectively reduces the number of training parameters when expanding reception fields of the network. Finally, based on the features extracted in previous blocks, the classification block is constructed to classify EEG tasks. The experiments are implemented on two public EEG datasets (BCI competition IV 2a and High gamma dataset) to validate the effectiveness of the proposed approach compared to the state-of-the-art methods. The competitive results demonstrate that our proposed method is a promising solution to improve the decoding performance of motor imagery BCIs.

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. The function of CYTOR was verified by cell-based experiments. To further identify lncRNAs with prognostic significance, a multivariate Cox proportional hazard regression analysis was performed. The identified lncRNAs were validated with an independent cohort using clinical feature relevance analysis and multivariate Cox regression analysis. RESULTS: We identified nine HNSCC-relevant lncRNAs likely to play pivotal roles in HNSCC onset and development. By functional enrichment analysis, we revealed that CYTOR might participate in the multistep pathological processes of cancer, such as ribosome biogenesis and maintenance of genomic stability. CYTOR was identified to be positively correlated with lymph node metastasis, and significantly negatively correlated with overall survival (OS) and disease free survival (DFS) of HNSCC patients. Moreover, CYTOR inhibited cell apoptosis following treatment with the chemotherapeutic drug diamminedichloroplatinum (DDP). HCG22, the most dramatically down-regulated lncRNA in tumor tissue, may function in epidermis differentiation. It was also significantly associated with several clinical features of patients with HNSCC, and positively correlated with patient survival. CYTOR and HCG22 maintained their prognostic values independent of several clinical features in multivariate Cox hazards analysis. Notably, validation either based on an independent HNSCC cohort or by laboratory experiments confirmed these findings. CONCLUSIONS: Our transcriptomic analysis suggested that dysregulation of these HNSCC-associated lncRNAs might be involved in HNSCC oncogenesis and progression. Moreover, CYTOR and HCG22 were confirmed as two independent prognostic factors for HNSCC patient survival, providing new insights into the roles of these lncRNAs in HNSCC as well as clinical applications.

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. The UOFR-MI algorithm, which uses not only the observed data themselves but also weak derivatives of the signals, is more powerful in model structure detection. The proposed approach combining the advantages of both the basis function expansion method and the UOFR-MI algorithm is proved to be capable of tracking the change of TV parameters effectively in both numerical simulations and the real EEG data.

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). In total, 241 participants (125 in the transcatheter closure group and 116 in the control group) were included in the study. In general, the PFO closure procedure was found to be safe. At 1 month after closure, 76.1% of patients returned for c-TCD evaluation; of these, 85.7% were downgraded to negative status or a grade-I shunt. Residual shunts and placebo effects were thought to resolve by 12 months post-procedure, when migraine impact was reported to decrease by 73.6%. Transcatheter PFO closure was demonstrated to be effective for the treatment of migraine by comparing HIT-6 scores between the transcatheter closure and control groups (p < 0.001). Our results suggest that transcatheter PFO closure is a safe and effective approach for the treatment of migraine in the Chinese population, especially in females with constant RLS. Clinical trial no. NCT02127294 (registered on April 29, 2014).

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. We evaluated 194 subjects for RLS detection with VA and MCA monitoring simultaneously. There was no significant difference between the positive rates of VA and MCA monitoring for RLS detection. c-TCD with VA monitoring could be an alternative for RLS detection, with high sensitivity and specificity both at rest and during the Valsalva manoeuvre.

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%, 36.9% and 47.3% for RS, CM and BM, respectively (p < 0.001). BM resulted in a significantly higher positive rate (p = 0.010), and there was a significant difference between the two different methods of VM in terms of the degree of RLS detection (p < 0.001). Further, the first microbubble occurred later during BM than CM (10.22 ± 3.77 s vs. 9.44 ± 4.36 s, p < 0.05). This modified maneuver is an alternative to the conventional one, especially for those who cannot perform the conventional maneuver adequately, but are highly suspected of having RLS.