A novel multi-feature fusion attention neural network for the recognition of epileptic EEG signals.

Epilepsy is a common chronic brain disorder. Detecting epilepsy by observing electroencephalography (EEG) is the main method neurologists use, but this method is time-consuming. EEG signals are non-stationary, nonlinear, and often highly noisy, so it remains challenging to recognize epileptic EEG signals more accurately and automatically. This paper proposes a novel classification system of epileptic EEG signals for single-channel EEG based on the attention network that integrates time-frequency and nonlinear dynamic features. The proposed system has three novel modules. The first module constructs the Hilbert spectrum (HS) with high time-frequency resolution into a two-channel parallel convolutional network. The time-frequency features are fully extracted by complementing the high-dimensional features of the two branches. The second module constructs a grayscale recurrence plot (GRP) that contains more nonlinear dynamic features than traditional RP, fed into the residual-connected convolution module for effective learning of nonlinear dynamic features. The third module is the feature fusion module based on a self-attention mechanism to assign optimal weights to different types of features and further enhance the information extraction capability of the system. Therefore, the system is named HG-SANet. The results of several classification tasks on the Bonn EEG database and the Bern-Barcelona EEG database show that the HG-SANet can effectively capture the contribution degree of the extracted features from different domains, significantly enhance the expression ability of the model, and improve the accuracy of the recognition of epileptic EEG signals. The HG-SANet can improve the diagnosis and treatment efficiency of epilepsy and has broad application prospects in the fields of brain disease diagnosis.

Discovering the neuronal dynamics in major depressive disorder using Hidden Markov Model.

Introduction: Major Depressive Disorder (MDD) is a leading cause of worldwide disability, and standard clinical treatments have limitations due to the absence of neurological evidence. Electroencephalography (EEG) monitoring is an effective method for recording neural activities and can provide electroneurophysiological evidence of MDD. Methods: In this work, we proposed a probabilistic graphical model for neural dynamics decoding on MDD patients and healthy controls (HC), utilizing the Hidden Markov Model with Multivariate Autoregressive observation (HMM-MAR). We testified the model on the MODMA dataset, which contains resting-state and task-state EEG data from 53 participants, including 24 individuals with MDD and 29 HC. Results: The experimental results suggest that the state time courses generated by the proposed model could regress the Patient Health Questionnaire-9 (PHQ-9) score of the participants and reveal differences between the MDD and HC groups. Meanwhile, the Markov property was observed in the neuronal dynamics of participants presented with sad face stimuli. Coherence analysis and power spectrum estimation demonstrate consistent results with the previous studies on MDD. Discussion: In conclusion, the proposed HMM-MAR model has revealed its potential capability to capture the neuronal dynamics from EEG signals and interpret brain disease pathogenesis from the perspective of state transition. Compared with the previous machine-learning or deep-learning-based studies, which regarded the decoding model as a black box, this work has its superiority in the spatiotemporal pattern interpretability by utilizing the Hidden Markov Model.

Astaxanthin, a carotenoid antioxidant, pretreatment alleviates cognitive deficits in aircraft noised mice by attenuating inflammatory and oxidative damage to the gut, heart and hippocampus.

BACKGROUND: We first explore whether aircraft noise (AN) induces cognitive deficit via inducing oxidative damage in multiple vital organs including intestines, hearts and hippocampus tissues. Second, we explore whether the AN-induced cognitive deficits and inflammatory and oxidative damage to multiple organs can be alleviated by Astaxanthin (AX) pretreatment. METHODS: Cognitive deficits were induced by subjecting the mice to AN 2 h daily for 7 consecutive days. An intragastrical dose of AX emulsifier (at the dose of daily feed intake [6 g] of a mouse three times weekly) was given to mice for consecutive 8 weeks prior to the start of AN. Cognitive functions were evaluated by using passive avoidance apparatus, Y-maze, Morris water maze and novel recognition test. Intestinal permeability was determined by measuring the intestinal clearance of fluorescein-isothiocyante. Evans Blue extravasation assay was used to measure the permeability of blood-brain-barrier. Inflammatory and oxidative damage to multiple organs were determined by measuring several pro-inflammatory cytokines and oxidative stress indicators in intestines; hearts and hippocampus. RESULTS: Mice treated with AN displayed exacerbated stress reactions, cognitive deficits, gut barrier hyperpermeability, increased upload of lipopolysaccharide translocation, systemic pro-inflammatory cytokines overproduction, blood-brain-barrier hyperpermeability, hippocampal neuroinflammation and increased levels of oxidative stress indicators in intestine, heart and hippocampus. All of the above-mentioned disorders caused by AN were significantly (P < 0.05) reversed by AX. CONCLUSIONS: Our data indicate that AX pretreatment alleviates cognitive deficits in aircraft noised mice by attenuating inflammatory and oxidative damage to intestines, hearts and hippocampal tissues.

MODENN: A Shallow Broad Neural Network Model Based on Multi-Order Descartes Expansion.

Deep neural networks have achieved great success in almost every field of artificial intelligence. However, several weaknesses keep bothering researchers due to its hierarchical structure, particularly when large-scale parallelism, faster learning, better performance, and high reliability are required. Inspired by the parallel and large-scale information processing structures in the human brain, a shallow broad neural network model is proposed on a specially designed multi-order Descartes expansion operation. Such Descartes expansion acts as an efficient feature extraction method for the network, improve the separability of the original pattern by transforming the raw data pattern into a high-dimensional feature space, the multi-order Descartes expansion space. As a result, a single-layer perceptron network will be able to accomplish the classification task. The multi-order Descartes expansion neural network (MODENN) is thus created by combining the multi-order Descartes expansion operation and the single-layer perceptron together, and its capacity is proved equivalent to the traditional multi-layer perceptron and the deep neural networks. Three kinds of experiments were implemented, the results showed that the proposed MODENN model retains great potentiality in many aspects, including implementability, parallelizability, performance, robustness, and interpretability, indicating MODENN would be an excellent alternative to mainstream neural networks.

Dual-Threshold-Based Microstate Analysis on Characterizing Temporal Dynamics of Affective Process and Emotion Recognition From EEG Signals.

Recently, emotion classification from electroencephalogram (EEG) data has attracted much attention. As EEG is an unsteady and rapidly changing voltage signal, the features extracted from EEG usually change dramatically, whereas emotion states change gradually. Most existing feature extraction approaches do not consider these differences between EEG and emotion. Microstate analysis could capture important spatio-temporal properties of EEG signals. At the same time, it could reduce the fast-changing EEG signals to a sequence of prototypical topographical maps. While microstate analysis has been widely used to study brain function, few studies have used this method to analyze how brain responds to emotional auditory stimuli. In this study, the authors proposed a novel feature extraction method based on EEG microstates for emotion recognition. Determining the optimal number of microstates automatically is a challenge for applying microstate analysis to emotion. This research proposed dual-threshold-based atomize and agglomerate hierarchical clustering (DTAAHC) to determine the optimal number of microstate classes automatically. By using the proposed method to model the temporal dynamics of auditory emotion process, we extracted microstate characteristics as novel temporospatial features to improve the performance of emotion recognition from EEG signals. We evaluated the proposed method on two datasets. For public music-evoked EEG Dataset for Emotion Analysis using Physiological signals, the microstate analysis identified 10 microstates which together explained around 86% of the data in global field power peaks. The accuracy of emotion recognition achieved 75.8% in valence and 77.1% in arousal using microstate sequence characteristics as features. Compared to previous studies, the proposed method outperformed the current feature sets. For the speech-evoked EEG dataset, the microstate analysis identified nine microstates which together explained around 85% of the data. The accuracy of emotion recognition achieved 74.2% in valence and 72.3% in arousal using microstate sequence characteristics as features. The experimental results indicated that microstate characteristics can effectively improve the performance of emotion recognition from EEG signals.

Upregulated expression of ROCK1 promotes cell proliferation by functioning as a target of miR-335-5p in non-small cell lung cancer.

Lung cancer is regarded as one of the dominant causes in cancer patients among men and women all over the world. Rho-associated coiled-coil forming protein kinase l (ROCK1) is characterized as pivotal downstream effectors of the small GTPase RhoA and reported to participate in tumor metastasis. miR-335-5p acts as tumor suppressor microRNA and is identified to be downregulated in tumor tissues. miR-335-5p/ROCK1 axis has been demonstrated to promote cell proliferation and metastasis in gastric cancer, hepatocellular carcinoma and so on. However, the role it plays in promoting cell proliferation in non-small cell lung cancer (NSCLC) is poorly understood. Here, we demonstrated that the upregulated expression of ROCK1 was highly correlated with downregulated expression of miR-335-5p in NSCLC tissues and cell lines. Mechanistically, Knockdown of ROCK1 inhibited cell proliferation in vitro, accompanied by cell cycle change confirmed by flow analysis. Furthermore, miR-335-5p can downregulate the ROCK1 expression by directly binding to the 3'-untranslated region in posttranscriptional level. In vivo animal model showed similar results. Our findings highlighted the crucial role that miR-335-5p acted as a tumor suppressor to modulate cell proliferation and cell cycle progression via downregulating ROCK1 expression. And this miR-335-5p/ROCK1 axis contributed to NSCLC pathogenesis and might be promising targets for NSCLC therapy.

Biological Computation Indexes of Brain Oscillations in Unattended Facial Expression Processing Based on Event-Related Synchronization/Desynchronization.

Estimation of human emotions from Electroencephalogram (EEG) signals plays a vital role in affective Brain Computer Interface (BCI). The present study investigated the different event-related synchronization (ERS) and event-related desynchronization (ERD) of typical brain oscillations in processing Facial Expressions under nonattentional condition. The results show that the lower-frequency bands are mainly used to update Facial Expressions and distinguish the deviant stimuli from the standard ones, whereas the higher-frequency bands are relevant to automatically processing different Facial Expressions. Accordingly, we set up the relations between each brain oscillation and processing unattended Facial Expressions by the measures of ERD and ERS. This research first reveals the contributions of each frequency band for comprehension of Facial Expressions in preattentive stage. It also evidences that participants have emotional experience under nonattentional condition. Therefore, the user's emotional state under nonattentional condition can be recognized in real time by the ERD/ERS computation indexes of different frequency bands of brain oscillations, which can be used in affective BCI to provide the user with more natural and friendly ways.