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Electroencephalography (EEG) and surface electromyography (sEMG) have been widely used in the rehabilitation training of motor function. However, EEG signals have poor user adaptability and low classification accuracy in practical applications, and sEMG signals are susceptible to abnormalities such as muscle fatigue and weakness, resulting in reduced stability. To improve the accuracy and stability of interactive training recognition systems, we propose a novel approach called the Attention Mechanism-based Multi-Scale Parallel Convolutional Network (AM-PCNet) for recognizing and decoding fused EEG and sEMG signals. Firstly, we design an experimental scheme for the synchronous collection of EEG and sEMG signals and propose an ERP-WTC analysis method for channel screening of EEG signals. Then, the AM-PCNet network is designed to extract the time-domain, frequency-domain, and mixed-domain information of the EEG and sEMG fusion spectrogram images, and the attention mechanism is introduced to extract more fine-grained multi-scale feature information of the EEG and sEMG signals. Experiments on datasets obtained in the laboratory have shown that the average accuracy of EEG and sEMG fusion decoding is 96.62%. The accuracy is significantly improved compared with the classification performance of single-mode signals. When the muscle fatigue level reaches 50% and 90%, the accuracy is 92.84% and 85.29%, respectively. This study indicates that using this model to fuse EEG and sEMG signals can improve the accuracy and stability of hand rehabilitation training for patients.

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder that is characterized by inattention, hyperactivity, and impulsivity. The neural mechanisms underlying ADHD remain inadequately understood, and current approaches do not well link neural networks and attention networks within brain networks. Our objective is to investigate the neural mechanisms related to attention and explore neuroimaging biological tags that can be generalized within the attention networks. In this paper, we utilized resting-state functional magnetic resonance imaging data to examine the differential functional connectivity network between ADHD and typically developing individuals. We employed a graph convolutional neural network model to identify individuals with ADHD. After classification, we visualized brain regions with significant contributions to the classification results. Our results suggest that the frontal, temporal, parietal, and cerebellar regions are likely the primary areas of dysfunction in individuals with ADHD. We also explored the relationship between regions of interest and attention networks, as well as the connection between crucial nodes and the distribution of positively and negatively correlated connections. This analysis allowed us to pinpoint the most discriminative brain regions, including the right orbitofrontal gyrus, the left rectus gyrus and bilateral insula, the right inferior temporal gyrus and bilateral transverse temporal gyrus in the temporal region, and the lingual gyrus of the occipital lobe, multiple regions of the basal ganglia and the upper cerebellum. These regions are primarily involved in the attention executive control network and the attention orientation network. Dysfunction in the functional connectivity of these regions may contribute to the underlying causes of ADHD.

Purpose EEG analysis of emotions is greatly significant for the diagnosis of psychological diseases and brain-computer interface (BCI) applications. However, the applications of EEG brain neural network for emotion classification are rarely reported and the accuracy of emotion recognition for cross-subject tasks remains a challenge. Thus, this paper proposes to design a domain invariant model for EEG-network based emotion identification. Methods A novel brain-inception-network based deep learning model is proposed to extract discriminative graph features from EEG brain networks. To verify its efficiency, we compared our proposed method with some commonly used methods and three types of brain networks. In addition, we also compared the performance difference between the EEG brain network and EEG energy distribution for emotion recognition. Result One public EEG-based emotion dataset (SEED) was utilized in this paper, and the classification accuracy of leave-one-subject-out cross-validation was adopted as the comparison index. The classification results show that the performance of the proposed method is superior to those of the other methods mentioned in this paper. Conclusion The proposed method can capture discriminative structural features from the EEG network, which improves the emotion classification performance of brain neural networks.

Abstract Chiral polycyclic indolines are widely present in natural products and have become the focus of extensive synthetic efforts. Here, we show the catalytic asymmetric dearomative [3 + 2] annulation of indoles with donor-acceptor aminocyclopropanes to construct tricyclic indolines. Key to the success of the reaction is the rational design of C 1-symmetric bifunctional tridentate imidazoline-pyrroloimidazolone pyridine ligand. Under 5 mol% of Ni(OTf)2-ligand complex, diverse tricyclic indolines containing cyclopentamine moieties are obtained in good chemoselectivities, high diastereoselectivities, and excellent enantioselectivities. An unusual cis-configuration ligand is superior to the trans-configuration ligand and the corresponding C 2-symmetric tridentate nitrogen ligands in the annulation reaction. Mechanistic studies by control experiments and density functional theory calculations reveal a dual activation manner, where Ni(II) complex activates the aminocyclopropane via coordination with the geminal diester, and imidazolidine NH forms a H-bond with the succinimide moiety.

This study aims to explore a method based on brain networks for implicit attention by using wavelet coherence as feature to identify individual targets in the visual field, find the optimal classification rhythm and time window, and investigate the relationship between the optimal rhythm and N2pc event-related potential. The study uses a weighted minimum norm estimate to locate the sources of the scalp EEG and reconstructs the source time series. The functional connectivity between brain areas during the visual search process is evaluated using wavelet coherence analysis, and a lateral difference network is constructed based on the difference in coherence values between the left and right visual fields. A support vector machine classifier is trained based on the wavelet coherence network features to identify the target in the left or right visual field. We also extract N2pc from the source activity data of the parieto-occipital brain region and record the time period in which N2pc occurred. The study finds that the best classification performance is achieved in the theta rhythm from 200 to 400 ms and achieved an average classification accuracy of 87% (chance level: 51.07%) in a serial search task. And this time window corresponds to the time period when N2pc appeared. The results show that the use of wavelet coherence analysis to evaluate the functional connectivity between brain areas during the visual search process provides a new approach for analyzing brain activity. The study's findings regarding the relationship between the N2pc and theta rhythm and the effectiveness of using wavelet coherence network features based on the theta rhythm for visual search classification contribute to the understanding of the neural mechanisms underlying visual search.

Attention and audiovisual integration are crucial subjects in the field of brain information processing. A large number of previous studies have sought to determine the relationship between them through specific experiments, but failed to reach a unified conclusion. The reported studies explored the relationship through the frameworks of early, late, and parallel integration, though network analysis has been employed sparingly. In this study, we employed time-varying network analysis, which offers a comprehensive and dynamic insight into cognitive processing, to explore the relationship between attention and auditory-visual integration. The combination of high spatial resolution functional magnetic resonance imaging (fMRI) and high temporal resolution electroencephalography (EEG) was used. Firstly, a generalized linear model (GLM) was employed to find the task-related fMRI activations, which was selected as regions of interesting (ROIs) for nodes of time-varying network. Then the electrical activity of the auditory-visual cortex was estimated via the normalized minimum norm estimation (MNE) source localization method. Finally, the time-varying network was constructed using the adaptive directed transfer function (ADTF) technology. Notably, Task-related fMRI activations were mainly observed in the bilateral temporoparietal junction (TPJ), superior temporal gyrus (STG), primary visual and auditory areas. And the time-varying network analysis revealed that V1/A1↔STG occurred before TPJ↔STG. Therefore, the results supported the theory that auditory-visual integration occurred before attention, aligning with the early integration framework.

Objectives The application of electroencephalography (EEG) to the study of major depressive disorder (MDD) is a common approach. However, there is no one-to-one correspondence between EEG and brain neural activity, and it is unclear whether single-trial EEG signals detect the cognitive neural activity of MDD. Methods Here, we used deep learning to explore this issue. Deep learning adopted in this paper was an end-to-end classification method named EEGNet which could update model parameters automatically based on the characteristics of the data and classify MDD and healthy subjects (HS) from single-trial EEG signals to obtain classification results above the chance level. Furthermore, the saliency map was used to analyze the neural network model and visualize the channels and time periods that contributed the most. Results Finally, EEGNet achieved an average classification accuracy of 61.4% in the four categories, and the result of feature visualization was consistent with the cognitive neural interpretation of existing studies. Conclusion The findings suggested that deep learning could help cognitive neuroscience explore neural activity.

Purpose Loneliness refers to an unhappy or depressed negative emotion in which individuals feel poor intimacy in quantity or quality in current social relationships. It is not only common among the elderly, but also widely among young adults. In this study, we investigated the effects of loneliness on visual attention of social and non-social stimuli. Materials and Methods Here, two different category judgments (emotions and buildings) were separately used to investigate the cognitive differences of individuals with loneliness when faced different (social and non-social) stimulus. Mixed design variance analysis was used to analyze the behavioral differences between the lonely group and the non-lonely group, and ERP technology was used to analyze the brain activity differences between the lonely group and the non-lonely group during the experiment. Results Finally, Results showed that not only the behavioral response of lonely individuals to negative emotions was significantly faster than non-lonely individuals, but also the N170 latency was significantly shorter, and the amplitude of left hemisphere P100 was significantly higher. These findings indicated that lonely individuals had attentional bias for negative emotions. This bias was manifested as early attention enhancement and middle attention alertness. Conclusion The present research further sheds light on the neral mechanisms of visual attentional bias in lonely individuals.

Purpose: Emotion is the reflection of individual's perception and understanding of various things, which needs the synergy of various brain regions. A large number of emotion decoding methods based on electroencephalogram (EEG) have been proposed. But extracting the most discriminative and cognitive features to construct a model is yet to be determined. This paper aims to construct a model that can extract the most discriminative and cognitive features. Materials and methods: Here, we collected EEG signals from 24 subjects in a musical emotion induction experiment. Then, an end-to-end branch LSTM-CNN (BLCNN) was used to extract emotion features from the laboratory dataset and DEAP dataset for emotion decoding. Finally, the extracted features were visualized on the laboratory dataset using saliency map. Result: The classification results showed that the accuracy of the three classification of the laboratory dataset was 95.78% ± 1.70%, and the accuracy of the four classification of the DEAP dataset was 80.97% ± 7.99%. We found that the discriminating features of positive emotion were distributed in the left hemisphere, at the same time, negative emotion features were distributed in the right hemisphere, where mainly in the frontal, parietal and occipital lobes. Conclusion: In this paper, we proposed a neural network model, namely BLCNN. The model obtained good results in laboratory dataset and DEAP dataset. Through the visual analysis of the features extracted by BLCNN, it was found that the features were consistent with emotional cognition. Therefore, this paper provided a new perspective for the practical application of human-computer emotional interaction.

As the single photon detection greatly improves the sensitivity of a traditional detector, it has enormous potentiality in distance measuring. Photon-counting laser ranging is able to extract the target's trajectory from large amounts of echo information in the case where prior information is unknown. This paper first provided the simulation of echo information produced by photon-counting laser ranging system, and then extracted the trajectory of the target through image processing. Comparing with the theoretical moving trajectory, the error is less than 5 m. Considering its advantages in short distance ranging, the photon-counting laser ranging system can be applied to the Beidou navigation system and solve its accuracy defects caused by environmental factors while cable patrolling.

IntroductionAcquisition of germinal vesicle (GV) stage oocytes for fertility preservation (FP) offers several benefits over in vivo matured oocyte cryopreservation following ovarian stimulation, particularly for cancer patients necessitating immediate treatment. Two FP approaches for GV oocytes are available: vitrification before in vitro maturation (IVM) at the GV stage (GV-VI) or post-IVM at the metaphase II (MII) stage (MII-VI). The optimal method remains to be determined.MethodsIn this study, mouse oocytes were collected without hormonal stimulation and vitrified either at the GV stage or the MII stage following IVM; non-vitrified in vitro matured MII oocytes served as the control (CON). The oocyte quality and developmental competence were assessed to obtain a better method for immediate FP.ResultsNo significant differences in IVM and survival rates were observed among the three groups. Nevertheless, GV-VI oocytes exhibited inferior quality, including abnormal spindle arrangement, mitochondrial dysfunction, and early apoptosis, compared to MII-VI and CON oocytes. Oocyte vitrification at the GV stage impacted maternal mRNA degradation during IVM. In addition, the GV-VI group demonstrated significantly lower embryonic developmental competence relative to the MII-VI group. RNA sequencing of 2-cell stage embryos revealed abnormal minor zygotic genome activation in the GV-VI group.ConclusionVitrification at the GV stage compromised oocyte quality and reduced developmental competence. Consequently, compared to the GV stage, oocyte vitrification at the MII stage after IVM is more suitable for patients who require immediate FP.

During growth and aging, the role of the hippocampus in memory depends on its interactions with related brain regions. Particularly, two subregions, anterior hippocampus (aHipp) and posterior hippocampus (pHipp), play different and critical roles in memory processing. However, age-related changes of hippocampus subregions on structure and function are still unclear. Here, we investigated age-related structural and functional characteristics of 106 participants (7-85 years old) in resting state based on fractional anisotropy (FA) and functional connectivity (FC) in aHipp and pHipp in the lifespan. The correlation between FA and FC was also explored to identify the coupling. Furthermore, the Wechsler Abbreviated Scale of Intelligence (WASI) was used to explore the relationship between cognitive ability and hippocampal changes. Results showed that there was functional separation and integration in aHipp and pHipp, and the number of functional connections in pHipp was more than that in aHipp across the lifespan. The age-related FC changes showed four different trends (U-shaped/inverted U-shaped/linear upward/linear downward). And around the age of 40 was a critical period for transformation. Then, FA analyses indicated that all effects of age on the hippocampal structures were nonlinear, and the white matter integrity of pHipp was higher than that of aHipp. In the functional-structural coupling, we found that the age-related FA of the right aHipp (aHipp.R) was negatively related to the FC. Finally, through the WASI, we found that the age-related FA of the left aHipp (aHipp.L) was positively correlated with verbal IQ (VERB) and vocabulary comprehension (VOCAB.T), the FA of aHipp.R was only positively correlated with VERB, and the FA of the left pHipp (pHipp.L) was only positively correlated with VOCAB.T. These FC and FA results supported that age-related normal memory changes were closely related to the hippocampus subregions. We also provided empirical evidence that memory ability was altered with the hippocampus, and its efficiency tended to decline after age 40.

BackgroundGlioblastoma is the most common malignancy of the neuroepithelium, yet existing research on this tumor is limited. LASSO is an algorithm of selected feature coefficients by which genes associated with glioblastoma prognosis can be obtained.MethodsGlioblastoma-related data were selected from the Cancer Genome Atlas (TCGA) database, and information was obtained for 158 samples, including 153 cancer samples and five samples of paracancerous tissue. In addition, 2,642 normal samples were selected from the Genotype-Tissue Expression (GTEx) database. Whole-gene bulk survival analysis and differential expression analysis were performed on glioblastoma genes, and their intersections were taken. Finally, we determined which genes are associated with glioma prognosis. The STRING database was used to analyze the interaction network between genes, and the MCODE plugin under Cytoscape was used to identify the highest-scoring clusters. LASSO prognostic analysis was performed to identify the key genes. Gene expression validation allowed us to obtain genes with significant expression differences in glioblastoma cancer samples and paracancer samples, and glioblastoma independent prognostic factors could be derived by univariate and multivariate Cox analyses. GO functional enrichment analysis was performed, and the expression of the screened genes was detected using qRT-PCR.ResultsWhole-gene bulk survival analysis of glioblastoma genes yielded 607 genes associated with glioblastoma prognosis, differential expression analysis yielded 8,801 genes, and the intersection of prognostic genes with differentially expressed genes (DEG) yielded 323 intersecting genes. PPI analysis of the intersecting genes revealed that the genes were significantly enriched in functions such as the formation of a pool of free 40S subunits and placenta development, and the highest-scoring clusters were obtained using the MCODE plug-in. Eight genes associated with glioblastoma prognosis were identified based on LASSO analysis: RPS10, RPS11, RPS19, RSL24D1, RPL39L, EIF3E, NUDT5, and RPF1. All eight genes were found to be highly expressed in the tumor by gene expression verification, and univariate and multivariate Cox analyses were performed on these eight genes to identify RPL39L and NUDT5 as two independent prognostic factors associated with glioblastoma. Both RPL39L and NUDT5 were highly expressed in glioblastoma cells.ConclusionTwo independent prognostic factors in glioblastoma, RPL39L and NUDT5, were identified.

ObjectiveTo explore the regulatory mechanism of immune prognostic factors in thyroid cancer.MethodsBased on the TCGA database and GEO database, this study used bioinformatics methods to study the potential regulatory mechanism of thyroid cancer prognosis, analyzed the differentially expressed genes and differential miRNAs between thyroid cancer and normal paracancerous tissues by R software, and constructed lasso risk factors. The immune prognostic factors of thyroid cancer were obtained from the model, and the miRDB website was used to predict the possibility of differential miRNA target binding of the immune prognostic factors and correlation analysis was performed, and finally verified by cell experiments.ResultsThere were 1413 differentially expressed genes between thyroid cancer and normal paracancerous tissues, among which 21 immune-related genes were prognostic factors with significant differences in expression; lasso risk model obtained AKAP12, APOC1, TIMP3, ADAMTS9, ANK2, HTRA3, SYNDIG1 , ADAMTS5 and DACT1 were nine prognostic factors. A total of 58 differential miRNAs were found in thyroid cancer tissues and non-cancerous tissues. The possibility of differential miRNA targeting and binding of immune prognostic factors on the miRDB website and cell experiments was analyzed.ConclusionsThe potential miRNA regulatory mechanism of immune prognostic factors in thyroid cancer has been explored.

Digital holography is an effective technology in image reconstruction as amplitude and phase information of cells can be acquired without any staining. In this paper, we propose a holographic technique with an improved Gerchberg-Saxton (GS) algorithm to reconstruct cell imaging based on phase reconstruction information. Comparative experiments are conducted on four specific models to investigate the effectiveness of the proposed method. The morphological parameters (such as shape, volume, and sphericity) of abnormal erythrocytes can be obtained by reconstructing cell hologram of urinary sediment. Notably, abnormal red blood cells can also be detected in mussy circumstances by the proposed method, owing to the significantly biophysical contrast (refractive index distribution and mass density) between two different cells. Therefore, this proposed method has a broad application prospect in cell image reconstruction and cell dynamic detection.

Background: This study aims to describe clinical and diagnostic phenotype and identify pathogenic variants of a female with unknown causes of infertility.Methods: Clinical assessment was performed for the phenotype diagnosis. Whole-exome sequencing (WES) and the followed cDNA-PCR sequencing were applied to identify the pathogenic variant and investigate the potentially aberrant mRNA splicing event. The pathogenicity of the variant was analysed using multiple in silico prediction tools, including the 3D protein remodelling. Quantitative RT-PCR (qRT-PCR) was performed to measure PATL2 mRNA expression in the peripheral blood leukocytes of the proband and controls.Results: The proband was diagnosed with the female infertility due to oocyte germinal vesicle (GV) arrest. A novel homozygous splice site variant of PATL2 (NM_001145112.2, c.871-1G>A), inherited from her asymptomatic heterozygous parents, was detected by WES. Sequencing of cDNA amplification products demonstrated that this variant resulted in the exon 10 skipping and in-frame loss of 54 nucleotides in the PATL2 transcript. Quantitative RT-PCR suggested that the mutant transcript escape the mRNA degradation.Conclusion: We identified a novel pathogenic homozygous splice site of PATL2 (c.871-1G>A) underlying the oocyte GV arrest phenotype and elucidated its molecular mechanism. This study expands the variant spectrum of PATL2 and benefits our understanding of its genotype-phenotype correlations.

Abstract Background Panax notoginseng triol saponins (PTS) has been used clinically for ischemic stroke therapy (IST) in China for more than 17 years due to its anti-platelet aggregation and neuro-protective effects, but its mechanism of action is not fully understand. In this study, anti-platelet aggregation-related protein analysis and computer simulations of drug-protein binding interactions were performed to explore the mechanism of the effects of PTS against ischemic stroke in an ischemia reperfusion model. Methods Three oral doses of PTS were administered in a model of middle cerebral artery occlusion (MCAO) in rats. Panax notoginseng total saponins (PNS) and a combination of PTS and aspirin were chosen for comparison. To evaluate therapeutic effects and explore possible mechanisms of anti-platelet aggregation, we measured cerebral infarct size and water content in brain tissue, histomorphological changes, expression of related factors (such as arachidonic acid metabolites) and platelet receptors in serum, as well as the binding affinity of PTS for platelet adhesion receptors. Results Compared with PNS, PTS showed a stronger and more potent anti-platelet aggregation effect in MCAO model rats. The combination of PTS and aspirin could reduce adverse gastrointestinal effects by regulating the TXA2/PGI2 ratio. We demonstrated for the first time that PTS was able to regulate Glycoprotein Ib-α (GP1BA) in a model animal. The binding of ginsenoside Rg1 and GP1BA could form a stable structure. Moreover, PTS could reduce von Willebrand factor (VWF)-mediated platelet adhesion to damaged vascular endothelium, and thus enhance the probability of anti-platelet aggregation and anti-thrombosis under pathological conditions. Conclusions Our results showed that GP1BA was closely related to the anti-platelet aggregation action of PTS, which provided new scientific and molecular evidence for its clinical application.

Abstract It was well known that there are e-particles and m-strings in the 3-dimensional (spatial dimension) toric code model, which realizes the 3-dimensional ℤ2 topological order. Recent mathematical result, however, shows that there are additional string-like topological defects in the 3-dimensional ℤ2 topological order. In this work, we construct all topological defects of codimension 2 and higher, and show that they form a braided fusion 2-category satisfying a braiding non-degeneracy condition.

Dendrobine-type sesquiterpenoid alkaloids from the Dendrobium plants have attracted many research interests due to their unique structures and remarkable physiological activities. This paper reviews the structural features and spectral (i.e., ultraviolet, infrared, mass and NMR) characteristics of dendrobines, and how their structures could be elucidated from the spectral data. The present work may provide a reference for further research and development of dendrobines.

Purpose. Peptic ulcer is a multifactorial and complex disease and affects a wide range of people worldwide. We provided a novel therapeutic approach for peptic ulcer and observed its effect. Methods. Peptic ulcer patients were enrolled from 2016 to 2017 in Chongqing and randomly assigned to two groups: a control group that used only rabeprazole and a platelet-rich plasma (PRP) group that received a combination therapy of autologous PRP (aPRP) and rabeprazole. The therapeutic effect was assessed via the ulcer size and symptom score. Results. A total of 27 patients were included (12 patients in the control group and 15 patients in the PRP group) in this study. Our results showed that all participants have healed in 30 days, and there was no significant difference in healing time between the PRP group and the control group in different independent variables. However, regression analysis revealed that the healing time was 6.99 days shorter in the PRP group than that in the control group, and patients with higher symptom scores in the initial examination need more time to heal during treatment. Endoscopic results showed that the repaired ulcer in the PRP group was more similar to the normal gastric mucosa tissue than that the control group. Conclusion. This study showed an encouraging preliminary result that aPRP has a positive result in patients with peptic ulcer and seems to be a better choice for refractory peptic ulcer treatment. Although further follow-up studies are needed to determine the duration of efficacy of aPRP, the approach will be helpful in improving the clinical treatment of peptic ulcer.

Attention deficit hyperactivity disorder (ADHD) is a common mental disorder in children, which is related to inattention and hyperactivity. These symptoms are associated with abnormal interactions of brain networks. We used resting-state functional magnetic resonance imaging (rs-fMRI) based on the graph theory to explore the topology property changes of brain networks between an ADHD group and a normal group. The more refined AAL_1024 atlas was used to construct the functional networks with high nodal resolution, for detecting more subtle changes in brain regions and differences among groups. We compared altered topology properties of brain network between the groups from multilevel, mainly including modularity at mesolevel. Specifically, we analyzed the similarities and differences of module compositions between the two groups. The results found that the ADHD group showed stronger economic small-world network property, while the clustering coefficient was significantly lower than the normal group; the frontal and occipital lobes showed smaller node degree and global efficiency between disease statuses. The modularity results also showed that the module number of the ADHD group decreased, and the ADHD group had short-range overconnectivity within module and long-range underconnectivity between modules. Moreover, modules containing long-range connections between the frontal and occipital lobes disappeared, indicating that there was lack of top-down control information between the executive control region and the visual processing region in the ADHD group. Our results suggested that these abnormal regions were related to executive control and attention deficit of ADHD patients. These findings helped to better understand how brain function correlates with the ADHD symptoms and complement the fewer modularity elaboration of ADHD research.

Flower teas are widely consumed across the world because of their beneficial health effects. The antioxidant activities of methanol extracts from ten common flower teas in China were evaluated using four antioxidant assays. The total phenolic (TPCs) and total flavonoid contents (TFCs) were also investigated. Most of these flower teas exhibited potent antioxidant effects, of which rosae rugosae flos exerted the strongest antioxidant effects in four assays. Rosae rugosae flos also exerted the highest TPC, while fragrans showed the highest TFC. Correction analysis indicated that phenolics play a key role in the antioxidant effect of flower teas, while flavonoids were poorly correlated with antioxidant activity. The results supported the consumption of flower teas as functional foods and their application as sources of natural antioxidants in the food industry.

C19-diterpenoid alkaloids have complex and diverse structures, posing great challenges for nuclear magnetic resonance (NMR)-based structural elucidation. Three C19-diterpenoid alkaloids, including taronenine E (1), chasmaconitine (2) and vilmorisine (3), were isolated from the roots of Aconitum taronense Fletcher et Lauener, and analyzed by various NMR spectroscopy methods (i.e., 1H and 13C NMR, DEPT, 1H-1H COSY, HSQC and HMBC). Complete assignments of the NMR chemical shifts were obtained, providing a good reference for the further research of C19-diterpenoid alkaloids.

Cytochalasins are a group of natural products from fungus, and have been attracting lots of research interests in the field of microbiology. Cytochalasins possess complex and diverse structures, making nuclear magnetic resonance (NMR) based structure elucidation difficult. Chaetoglobosin F is a 10-indol cytochalasin isolated from endophytic Chaetomium globosum growing in the seeds of Panax notoginseng. This paper reported the NMR data chaetoglobosin F, along with the summarization of NMR features of 10-indol cytochalasins, providing a good reference for the further research on cytochalasins.

Majority of diterpenoid alkaloids are C19-diterpenoid alkaloids found in the plants from the genera Aconitum, Delphinium and Consolida (Ranunculaceae). C19-diterpenoid alkaloids have attached lots of research interests due to their complex structures and remarkable pharmacological activities. This paper reviewed the structural features of natural C19-diterpenoid alkaloids reported in literature from 2010 to 2018, focusing on the NMR spectral characteristics of different subtypes of C19-diterpenoid alkaloids and how their structures could be elucidated from the NMR data. The work may provide a reference for the further research and development of C19-diterpenoid alkaloids.

Disclosing the complex relationships effectively between paired brain regions played a significant role in measuring the brain functional connectivity and exploring brain topological structures. Even though Pearson correlation coefficient (PCC) has been widely used to construct functional brain networks in the previous studies, it was mainly sensitive to linear associations. Therefore, maximal information coefficient (MIC) was first utilized to make up this weakness of PCC to construct electroencephalography (EEG) connectivity in the current study. The simulation results showed that MIC could capture certain relationships which PCC failed to detect. Furthermore, brain network properties changed with various thresholds under the resting-state EEG, and the comparison analysis of network properties illustrated that MIC and PCC could capture different aspects of connections between paired brain regions. These findings indicated that MIC could be a complementary method of PCC for the construction of scalp resting-state EEG connectome and provided a novel tool to reveal the potential mechanisms of brain networks.

N2pc is defined as a negative event-related potential component that appears after about 250 ms at posterior electrodes contralateral to a target's location in visual search, which can be used to measure attentional shifts between bilateral visual hemifields and locate the spatial location of lateral targets. However, the waves between the left and right hemispheres elicited by lateral targets usually exhibit a small amplitude difference and strong synchronicity, which may lead to low classification performance. Therefore, the present study explored the feasibility of a multiple correlated components analysis (MCORCA) methods to identify the lateral targets in visual search tasks with a single trial, which could weight the target signals by spatial filters to enlarge the amplitude difference between bilateral hemispheres and extract the linear combinations of multiple channels across trials with an optimal subset of correlated components to avoid the loss of efficient information. The classification rate achieved 82% with a single short-duration trial when using the proposed method with Leave-one-out-cross-validation (LOOCV). The findings demonstrated that the MCORCA-based methods could be used to improve the classification performance for the N2pc-based brain-computer interfaces (BCI) in visual search.

Previous studies have shown that different frequency band oscillations are associated with cognitive processing such as working memory (WM). Electroencephalogram (EEG) coherence and graph theory can be used to measure functional connections between different brain regions and information interaction between different clusters of neurons. At the same time, it was found that better cognitive performance of individuals indicated stronger small-world characteristics of resting-state WM networks. However, little is known about the neural synchronization of the retention stage during ongoing WM tasks (i.e., online WM) by training on the whole-brain network level. Therefore, combining EEG coherence and graph theory analysis, the present study examined the topological changes of WM networks before and after training based on the whole brain and constructed differential networks with different frequency band oscillations (i.e., theta, alpha, and beta). The results showed that after WM training, the subjects’ WM networks had higher clustering coefficients and shorter optimal path lengths than before training during the retention period. Moreover, the increased synchronization of the frontal theta oscillations seemed to reflect the improved executive ability of WM and the more mature resource deployment; the enhanced alpha oscillatory synchronization in the frontoparietal and fronto-occipital regions may reflect the enhanced ability to suppress irrelevant information during the delay and pay attention to memory guidance; the enhanced beta oscillatory synchronization in the temporoparietal and frontoparietal regions may indicate active memory maintenance and preparation for memory-guided attention. The findings may add new evidence to understand the neural mechanisms of WM on the changes of network topological attributes in the task-related mode.

Two C19-diterpenoid alkaloids, homochasmanine (compound 1) and 14-debenzoylfranchetine (compound 2), were isolated from the roots of Aconitum handelianum. Their structures were elucidated by one-dimensional and two-dimensional nuclear magnetic resonance (NMR) techniques, including 1H NMR, 13C NMR, DEPT, 1H-1H COSY, HSQC, HMBC and NOESY. The 1H and 13C NMR signals of the compounds were completely assigned.

Radiotherapy (RT) is the standard treatment for nasopharyngeal carcinoma, which often causes inevitable brain injury in the process of treatment. The majority of patients has no abnormal signal or density change of the conventional magnetic resonance imaging (MRI) and computed tomography (CT) examination in the long-term follow-up after radiation therapy. However, when there is a visible CT and conventional MR imaging changes, the damage often has been severe and lack of effective treatments, seriously influencing the prognosis of patients. Therefore, the present study aimed to investigate the abnormal changes in nasopharyngeal carcinoma (NPC) patients after RT. In the present study, we exploited the machine learning framework which contained two parts: feature extraction and classification to automatically detect the brain injury. Our results showed that the method could effectively identify the abnormal regions reduced by radiotherapy. The highest classification accuracy was 82.5 % in the abnormal brain regions. The parahippocampal gyrus was the highest accuracy region, which suggested that the parahippocampal gyrus could be most sensitive to radiotherapy and involved in the pathogenesis of radiotherapy-induced brain injury in NPC patients.

The present study aimed to investigate individual differences of causal connectivity between brain regions in attention deficit hyperactivity disorder (ADHD) which was a psychiatric disorder. Resting-state functional magnetic resonance imaging (R-fMRI) data of typically-developing controls (TDC) children group and combined ADHD (ADHD-C) children group were distinguished by the support vector machine (SVM) with linear kernel function, based on regional homogeneity (ReHo), amplitude of low frequency fluctuation (ALFF) and fractional ALFF (FALFF). The highest classification accuracy yielded by ReHo was 90.91 %. Furthermore, the granger causality analysis (GCA) method based on the classified weight map of regions of interesting (ROIs) showed that five causal flows existed significant difference between TDC and ADHD-C. That is, the averaged GCA values of three causal connections (i.e. left VLPFC à left CC1, right PoCG à left CC1, and right PoCG à right CC2) for ADHD-C were separately stronger than those for TDC. And the other two connections (i.e. right FEF à right SOG and right CC1 à right SOG) were weaker for ADHD-C than those for TDC. In addition, only two causality flows (i.e. left VLPFC à left CC1 and right PoCG à right CC2) presented that their GCA values were positively correlation with ADHD index scores, respectively. Our findings revealed that ADHD children represented widespread abnormalities in the causality connectivity, especially involved in the attention and memory related regions. And further provided evidence that the potential neural causality flows could play a key role in characterizing individual’s ADHD.

The amygdala, which is involved in human social information processing and socio-emotional response neuronal circuits, is segmented into three subregions that are responsible for perception, affiliation, and aversion. Though there is different functional and effective connectivity (EC) among these networks, age-related structural changes and associations between structure and function within the amygdala remain unclear. Here, we used diffusion tensor imaging (DTI) data (106 participants) to investigate age-related structural changes in fractional anisotropy (FA) of amygdalar subregions. We also examined the relationship between FA and EC within the subregions. We found that the FA of the amygdalar subregions exhibited inverted-U-shape trends with age. Moreover, over the human lifespan, there were negative correlations between the FA of the right ventrolateral amygdala (VLA.R) and the Granger-based EC (GC) of VLA.R → perception network (PerN), the FA of the VLA.R and the GC of the net flow from VLA.R → PerN, and the FA of the left dorsal amygdala (DorA.L) and the GC of the aversion network (AveN). Conversely, there was a positive correlation between the FA of the DorA.L and the GC of the net flow from DorA.L → AveN. Our results suggest that age-related changes in the function of the brain are constrained by the underlying white matter architectures, while the functional information flow changes influence white matter structure. This work increases our understanding of the neuronal mechanisms in the maturation and aging process.

Whether an event-related potential (ERP), N170, related to facial recognition was modulated by emotion has always been a controversial issue. Some researchers considered the N170 to be independent of emotion, whereas a recent study has shown the opposite view. In the current study, electroencephalogram (EEG) recordings while responding to facial pictures with emotion were utilized to investigate whether the N170 was modulated by emotion. We found that there was a significant difference between ERP trials with positive and negative emotions of around 170 ms at the occipitotemporal electrodes (i.e., N170). Then, we further proposed the application of the single-trial N170 as a feature for the classification of facial emotion, which could avoid the fact that ERPs were obtained by averaging most of the time while ignoring the trial-to-trial variation. In order to find an optimal classifier for emotional classification with single-trial N170 as a feature, three types of classifiers, namely, linear discriminant analysis (LDA), L1-regularized logistic regression (L1LR), and support vector machine with radial basis function (RBF-SVM), were comparatively investigated. The results showed that the single-trial N170 could be used as a classification feature to successfully distinguish positive emotion from negative emotion. L1-regularized logistic regression classifiers showed a good generalization, whereas LDA showed a relatively poor generalization. Moreover, when compared with L1LR, the RBF-SVM required more time to optimize the parameters during the classification, which became an obstacle while applying it to the online operating system of brain-computer interfaces (BCIs). The findings suggested that face-related N170 could be affected by facial expression and that the single-trial N170 could be a biomarker used to monitor the emotional states of subjects for the BCI domain.

Using the scalp time-varying network method, the present study is the first to investigate the temporal influence of the reference on N170, a negative event-related potential component (ERP) appeared about 170 ms that is elicited by facial recognition, in the network levels. Two kinds of scalp electroencephalogram (EEG) references, namely, AR (average of all recording channels) and reference electrode standardization technique (REST), were comparatively investigated via the time-varying processing of N170. Results showed that the latency and amplitude of N170 were significantly different between REST and AR, with the former being earlier and smaller. In particular, the information flow from right temporal-parietal P8 to left P7 in the time-varying network was earlier in REST than that in AR, and this phenomenon was reproduced by simulation, in which the performance of REST was closer to the true case at source level. These findings indicate that reference plays a crucial role in ERP data interpretation, and importantly, the newly developed approximate zero-reference REST would be a superior choice for precise evaluation of the scalp spatio-temporal changes relating to various cognitive events.

About 35 million people worldwide were suffered from Alzheimer’s disease (AD) in 2014 and the number of patients was expected to increase by 4-fold in 2050. As a neurodegenerative disease impacting our society, the pathogenesis and prognosis of AD have not yet fully understood. Senile plaque is generally regarded as one of the hallmarks of the disease, which may be due to the imbalance of Aβ peptides in the brain. Over the last decades, studies of early onset familial AD have led us to a deeper understanding of the genetics and molecular biology of AD. There is increasing evidence to suggest that the pathogenesis of AD is more likely to be caused by multiple genetic mutations. However, the precise genetic component leading to AD pathogenesis remains unclear. In this review, we will briefly introduce the classification of AD in the context of genetics. Then we will discuss the gene mutations in chromosome 21, 19 and presenilin as well as their links to Aβ peptides. Imaging data will be discussed alongside to complement the associated structural and physiological changes in the brain. It is our hope that future research in genetics will continue to enhance our understanding of the pathogenesis of AD and the mechanisms leading to the formation of senile plaques.

Resting-state functional magnetic resonance imaging was utilized to measure the amplitude low frequency fluctuations (ALFF) in human subjects with Alzheimer’s disease (AD) and normal control (NC). Two specific frequency bands (Slow5: 0.01-0.027Hz and Slow4: 0.027-0.073Hz) were analysed in the main cognitive control related four subregions of the right ventral lateral prefrontal cortex (VLPFC), i.e. IFJ, posterior-VLPFC, mid-VLPFC, and anterior-VLPFC. Differences in ALFF values between the AD and the NC group were found throughout the subregions of the right VLPFC. Compared to normal control group, decreased ALFF values were observed in AD patients in the IFJ (in two given frequency bands), and the mid-VLPFC (in Slow5). In contrast, increased ALFF valued were observed in AD patients in the posterior- and anterior-VLPFC (in both Slow5 and Slow4), and also in the mid-VLPFC in Slow4. Moreover, significant ALFF differences between the IFJ and three other subregions of the right VLPFC were found. Furthermore, ALFF values in the right VLPFC showed significant correlations with the time course of disease. Taken together, our findings suggest that AD patients have largely abnormalities in intrinsic neural oscillations which are in line with the AD pathophysiology, and further reveal that the abnormalities are dependent on specific frequency bands. Thus, frequency-domain analyses of the ALFF may provide a useful tool to investigate the AD pathophysiology.

Spectral entropy, which was generated by applying the Shannon entropy concept to the power distribution of the Fourier-transformed electroencephalograph (EEG), was utilized to measure the uniformity of power spectral density underlying EEG when subjects performed the working memory tasks twice, i.e., before and after training. According to Signed Residual Time (SRT) scores based on response speed and accuracy trade-off, 20 subjects were divided into two groups, namely high-performance and low-performance groups, to undertake working memory (WM) tasks. We found that spectral entropy derived from the retention period of WM on channel FC4 exhibited a high correlation with SRT scores. To this end, spectral entropy was used in support vector machine classifier with linear kernel to differentiate these two groups. Receiver operating characteristics analysis and leave-one out cross-validation (LOOCV) demonstrated that the averaged classification accuracy (CA) was 90.0 and 92.5% for intra-session and inter-session, respectively, indicating that spectral entropy could be used to distinguish these two different WM performance groups successfully. Furthermore, the support vector regression prediction model with radial basis function kernel and the root-mean-square error of prediction revealed that spectral entropy could be utilized to predict SRT scores on individual WM performance. After testing the changes in SRT scores and spectral entropy for each subject by short-time training, we found that 16 in 20 subjects’ SRT scores were clearly promoted after training and 15 in 20 subjects’ SRT scores showed consistent changes with spectral entropy before and after training. The findings revealed that spectral entropy could be a promising indicator to predict individual’s WM changes by training and further provide a novel application about WM for brain–computer interfaces.

Loneliness is broadly described as a negative emotional response resulting from the differences between the actual and desired social relations of an individual, which is related to the neural responses in connection with social and emotional stimuli. Prior research has discovered that some neural regions play a role in loneliness. However, little is known about the differences among individuals in loneliness and the relationship of those differences to differences in neural networks. The current study aimed to investigate individual differences in perceived loneliness related to the causal interactions between resting-state networks (RSNs), including the dorsal attentional network (DAN), the ventral attentional network (VAN), the affective network (AfN) and the visual network (VN). Using conditional granger causal analysis of resting-state fMRI data, we revealed that the weaker causal flow from DAN to VAN is related to higher loneliness scores, and the decreased causal flow from AfN to VN is also related to higher loneliness scores. Our results clearly support the hypothesis that there is a connection between loneliness and neural networks. It is envisaged that neural network features could play a key role in characterizing the loneliness of an individual.

Mapping human brain networks provides a basis for studying brain function and dysfunction, and thus has gained significant interest in recent years. However, modeling human brain networks still faces several challenges including constructing networks at multiple spatial scales and finding common corresponding networks across individuals. As a consequence, many previous methods were designed for a single resolution or scale of brain network, though the brain networks are multi-scale in nature. To address this problem, this paper presents a novel approach to constructing multi-scale common structural brain networks from DTI data via an improved multi-scale spectral clustering applied on our recently developed and validated DICCCOLs (Dense Individualized and Common Connectivity-based Cortical Landmarks). Since the DICCCOL landmarks possess intrinsic structural correspondences across individuals and populations, we employed the multi-scale spectral clustering algorithm to group the DICCCOL landmarks and their connections into sub-networks, meanwhile preserving the intrinsically-established correspondences across multiple scales. Experimental results demonstrated that the proposed method can generate multi-scale consistent and common structural brain networks across subjects, and its reproducibility has been verified by multiple independent datasets. As an application, these multi-scale networks were used to guide the clustering of multi-scale fiber bundles and to compare the fiber integrity in schizophrenia and healthy controls. In general, our methods offer a novel and effective framework for brain network modeling and tract-based analysis of DTI data.