Brain networks are spatiotemporal phenomena that dynamically vary over time. Functional imaging approaches strive to noninvasively estimate these underlying processes. Here, we propose a novel source ...imaging approach that uses high-density EEG recordings to map brain networks. This approach objectively addresses the long-standing limitations of conventional source imaging techniques, namely, difficulty in objectively estimating the spatial extent, as well as the temporal evolution of underlying brain sources. We validate our approach by directly comparing source imaging results with the intracranial EEG (iEEG) findings and surgical resection outcomes in a cohort of 36 patients with focal epilepsy. To this end, we analyzed a total of 1,027 spikes and 86 seizures. We demonstrate the capability of our approach in imaging both the location and spatial extent of brain networks from noninvasive electrophysiological measurements, specifically for ictal and interictal brain networks. Our approach is a powerful tool for noninvasively investigating large-scale dynamic brain networks.
High-frequency oscillations (HFOs) are a promising biomarker for localizing epileptogenic brain and guiding successful neurosurgery. However, the utility and translation of noninvasive HFOs, although ...highly desirable, is impeded by the difficulty in differentiating pathological HFOs from nonepileptiform high-frequency activities and localizing the epileptic tissue using noninvasive scalp recordings, which are typically contaminated with high noise levels. Here, we show that the consistent concurrence of HFOs with epileptiform spikes (pHFOs) provides a tractable means to identify pathological HFOs automatically, and this in turn demarks an epileptiform spike subgroup with higher epileptic relevance than the other spikes in a cohort of 25 temporal epilepsy patients (including a total of 2,967 interictal spikes and 1,477 HFO events). We found significant morphological distinctions of HFOs and spikes in the presence/absence of this concurrent status. We also demonstrated that the proposed pHFO source imaging enhanced localization of epileptogenic tissue by 162% (∼5.36 mm) for concordance with surgical resection and by 186% (∼12.48 mm) with seizure-onset zone determined by invasive studies, compared to conventional spike imaging, and demonstrated superior congruence with the surgical outcomes. Strikingly, the performance of spike imaging was selectively boosted by the presence of spikes with pHFOs, especially in patients with multitype spikes. Our findings suggest that concurrent HFOs and spikes reciprocally discriminate pathological activities, providing a translational tool for noninvasive presurgical diagnosis and postsurgical evaluation in vulnerable patients.
This paper is concerned with the influence of the pilot time delay on the flight performance. Simulations of a typical flight mission are implemented with a structured pilot model. As comparison, ...experiments with test pilots are carried out. The results are analyzed with a quantification method which gives a quantitative evaluation of the flight performance. Both of the simulation and the experiment show a negative correlation between the flight performance and the pilot time delay. The results of the experiments verify the reliability of the simulation method and the pilot model. The results also give a safety margin of the pilot time delay.
Objective
Drug‐resistant focal epilepsy is widely recognized as a network disease in which epileptic seizure propagation is likely coordinated by different neuronal oscillations such as low‐frequency ...activity (LFA), high‐frequency activity (HFA), or low‐to‐high cross‐frequency coupling. However, the mechanism by which different oscillatory networks constrain the propagation of focal seizures remains unclear.
Methods
We studied focal epilepsy patients with invasive electrocorticography (ECoG) recordings and compared multilayer directional network interactions between focal seizures either with or without secondary generalization. Within‐frequency and cross‐frequency directional connectivity were estimated by an adaptive directed transfer function and cross‐frequency directionality, respectively.
Results
In the within‐frequency epileptic network, we found that the seizure onset zone (SOZ) always sent stronger information flow to the surrounding regions, and secondary generalization was accompanied by weaker information flow in the LFA from the surrounding regions to SOZ. In the cross‐frequency epileptic network, secondary generalization was associated with either decreased information flow from surrounding regions’ HFA to SOZ's LFA or increased information flow from SOZ's LFA to surrounding regions’ HFA.
Interpretation
Our results suggest that the secondary generalization of focal seizures is regulated by numerous within‐ and cross‐frequency push–pull dynamics, potentially reflecting impaired excitation–inhibition interactions of the epileptic network. ANN NEUROL 2019;86:683–694
This study proposed a new method for multi-focus image fusion using hybrid wavelet and classifier. The image fusion process was formulated as a two-class classification problem: in and out-of-focus ...classes. First, a six-dimensional feature vector was extracted using sub-bands of dual-tree complex wavelet transform (DT-CWT) coefficients from the source images, which were then projected by a trained two-class support vector machine (SVM) to the class labels. A bacterial foraging optimization algorithm (BFOA) was developed to obtain the optimal parameters of the SVM. The output of the classification system was used as a decision matrix for fusing high-frequency wavelet coefficients from multi-focus source images in different directions and decomposition levels of the DT-CWT. After the high and low-frequency coefficients of the source images were fused, the final fused image was obtained using the inverse DT-CWT. Several existing methods were compared with the proposed method. Experimental results showed that our presented method outperformed the existing methods, in visual effect and in objective evaluation.
Molybdenum disulfide (MoS2), a typical earth-abundant material, is an excellent candidate for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), which fundamentally rely on ...the regulation of the morphology and electronic structure of MoS2. Herein, Mn-doped amorphous MoS2 coated on Mn-doped crystalline Ni3S2 nanorods (Mn–Ni3S2@MoS2), rationally designed core–shell nanorods, have been fabricated via a facile one-step hydrothermal method as highly efficient bifunctional activities for HER and OER in alkaline solution. The target electrodes deliver a high current density of 100 mA cm–2 at a low overpotential of 187 and 310 mV for HER and OER, respectively, outperforming most MoS2-based catalysts. Moreover, a water-splitting cell based on the Mn–Ni3S2@MoS2 electrode requires a voltage of 1.45 V to reach a current density of 10 mA cm–2, which is superior to the state-of-the-art one of those based on noble metal Pt/C–NF∥RuO2–NF and non-noble metal catalysts. The overall enhanced bifunctional catalytic performance is mainly attributed to the abundant catalytically active sites provided by the Mn-doped amorphous MoS2 and the fast pathway for electron/proton transfer facilitated by the Mn-doped crystalline Ni3S2 nanorods. The incorporated Mn dopants and assembled Ni3S2/MoS2 heterostructure effectively regulate the electronic structure with redistributed charge within the core–shell Mn–Ni3S2@MoS2 electrode.
Cognitive state, which is the inner mental state of a person while interacting with an artificial system through man-machine interface, can be affected by various factors, such as fatigue, stress, ...mental workload, attention deficit, and executive function, among others, which can lead to errors, accidents, or even disasters. One practical solution to this problem is to monitor and recognize the cognitive state of subjects via physiological signals. In this study, a hybrid adaptive flower pollination algorithm-Gaussian process model is proposed to recognize the cognitive state of in-flight pilots. Instead of using the traditional conjugate gradient technique to find optimal hyperparameters, an improved flower pollination algorithm is proposed. The adaptive Lévy strategy is then used to increase the robustness of this algorithm, as well as to enhance the global optimization and generalization capability of the Gaussian process model. In addition to conventional features in the time-frequency domain, a novel set of features involving wavelet singular entropy and autoregressive-moving average entropy is proposed to improve classification accuracy. Experiments are performed through flight simulations in a full flight simulator with six degrees of freedom. Comparable experimental results validate the feasibility of the proposed method for recognizing cognitive state and provide a wide range of conclusions on the feature selection and feature patterns of cognitive state.