Construction and analysis of functional brain network based on emotional electroencephalogram

Networks play an important role in studying structure or functional connection of various brain areas, and explaining mechanism of emotion. However, there is a lack of comprehensive analysis among different construction methods nowadays. Therefore, this paper studies the impact of different emotions on connection of functional brain networks (FBNs) based on electroencephalogram (EEG). Firstly, we defined electrode node as brain area of vicinity of electrode to construct 32-node small-scale FBN. Pearson correlation coefficient (PCC) was used to construct correlation-based FBNs. Phase locking value (PLV) and phase synchronization index (PSI) were utilized to construct synchrony-based FBNs. Next, global properties and effects of emotion of different networks were compared. The difference of synchrony-based FBN concentrates in alpha band, and the number of differences is less than that of correlation-based FBN. Node properties of different small-scale FBNs have significant differences, offering a new basis for feature extraction of recognition regions in emotional FBNs. Later, we made partition of electrode nodes and 10 new brain areas were defined as regional nodes to construct 10-node large-scale FBN. Results show the impact of emotion on network clusters on the right forehead, and high valence enhances information processing efficiency of FBN by promoting connections in brain areas.

Large-scale prediction of key dynamic interacting proteins in multiple cancers

Tracking cancer dynamic protein-protein interactions (PPIs) and deciphering their pathogenesis remain a challenge. We presented a dynamic PPIs' hypothesis: permanent and transient interactions might achieve dynamic switchings from normal cells to malignancy, which could cause maintenance functions to be interrupted and transient functions to be sustained. Based on the hypothesis, we first predicted >1400 key cancer genes (KCG) by applying PPI-express we proposed to 18 cancer gene expression datasets. We then further screened out key dynamic interactions (KDI) of cancer based on KCG and transient and permanent interactions under both conditions. Two prominent functional characteristics, "Cell cycle-related" and "Immune-related", were presented for KCG, suggesting that these might be their general characteristics. We found that, compared to permanent to transient KDI pairs (P2T) in the network, transient to permanent (T2P) have significantly higher edge betweenness (EB), and P2T pairs tending to locate intra-functional modules may play roles in maintaining normal biological functions, while T2P KDI pairs tending to locate inter-modules may play roles in biological signal transduction. It was consistent with our hypothesis. Also, we analyzed network characteristics of KDI pairs and their functions. Our findings of KDI may serve to understand and explain a few hallmarks of cancer.

Altered resting-state fMRI signals and network topological properties of bipolar depression patients with anxiety symptoms

BACKGROUND

This study aims to explore the changes in functional neuroimaging in bipolar depression patients with anxiety symptoms (BDP-A).

METHODS

Forty-five BDP-A patients, 22 bipolar depression patients without anxiety symptoms (BDP-NA), and 48 healthy controls (HC) were finally involved. The low-frequency oscillation characteristics, functional connectivity (FC), and network properties among the three groups of participants were analyzed.

RESULTS

Compared with the BDP-NA group, BDP-A patients exhibited significantly decreased amplitude of low-frequency fluctuation (ALFF) in the left middle frontal gyrus (MFG), superior occipital gyrus, and inferior parietal, but supramarginal and angular gyri (IPL). Enhanced FC from left IPL to middle temporal gyrus, from left precentral gyrus (PreCG) to bilateral angular gyri, medial superior frontal gyrus, and left superior frontal gyrus (SFG)/MFG were also revealed. Compared with HC, the BDP-A group showed remarkably increased ALFF in the left MFG/PreCG, right superior parietal gyrus, while decreased ALFF in the left inferior frontal gyrus, opercular part, and SFG. In addition, higher regional homogeneity in the left MFG/PreCG was found.

LIMITATIONS

The limitations are as follows: (1) relatively small sample size; (2) not all the patients were drug-naive; (3) lack of pure anxiety disorder patients as a controlled group; (4) mental health conditions of HC were not systemic evaluated.

CONCLUSIONS

BDP-A patients showed significant differences in resting-state fMRI properties when compared with BDP-NA or HC group. These results may infer the dysfunction of the dorsal attention network, the default network, and the fronto-limbic system as well as disrupted brain network efficiency in BDP-A patients.

The effects of lesion and treatment-related recovery on functional network modularity in post-stroke dysgraphia

A better understanding of the neural network properties that support cognitive recovery after a brain lesion is important for our understanding of human neuroplasticity and may have valuable clinical implications. In fifteen individuals with chronic, acquired written language deficits subsequent to left-hemisphere stroke, we used task-based functional connectivity to evaluate the relationship between the graph-theoretic measures (modularity, participation coefficient and within-module degree z-score) and written language production accuracy before and after behavioral treatment. A reference modular structure and local and global hubs identified from healthy controls formed the basis of the analyses. Overall, the investigation revealed that less modular networks with greater global and lower local integration were associated with greater deficit severity and lower response to treatment. Furthermore, we found treatment-induced increases in modularity and local integration measures. In particular, local integration within intact ventral occipital-temporal regions of the spelling network showed the greatest increase in local integration following treatment. This investigation significantly extends previous research by using task-based (rather than resting-state) functional connectivity to examine a larger set of network characteristics in the evaluation of treatment-induced recovery and by including comparisons with control participants. The findings demonstrate the relevance of network modularity for understanding the neuroplasticity supporting functional neural reorganization.

Spatial Vulnerability of Network Systems under Spatially Local Hazards

A hazard is often spatially local in a network system, but its impact can spread out through network topology and become global. To qualitatively and quantitatively assess the impact of spatially local hazards on network systems, this article develops a new spatial vulnerability model by taking into account hazard location, area covered by hazard, and impact of hazard (including direct impact and indirect impact), and proposes an absolute spatial vulnerability index (ASVI) and a relative spatial vulnerability index (RSVI). The relationship between the new model and some relevant traditional network properties is also analyzed. A case study on the spatial vulnerability of the Chinese civil aviation network system is conducted to demonstrate the effectiveness of the model, and another case study on the Beijing subway network system to verify its relationship with traditional network properties.

The analyses of human inherited disease and tissue-specific proteins in the interaction network

OBJECTIVES

With the announcement of human proteome and interaction data, it becomes possible to comprehensively investigate the tissue-expression and network properties of inherited disease proteins. In this study, our goal was to develop methods to map the disease and expression data and analyze the disorder-tissue associations.

METHODS

In this paper, we manually classified the human disease proteins into 22 disorder classes and systematically analyzed the properties of disease proteins in different disorder classes. Then, we investigated the similarity of different disorder classes by computing the overlap of different disorder proteins and networks. We proposed two novel measures, Enrichment Ratio and P-value for comparative analysis of disease proteins across tissues and revealed the associations between disorder classes and tissues/cells.

RESULTS

Compared with non-disease proteins, disease proteins tend to express in more tissues, have higher expression levels and interact with more other proteins in the network. The overlap percentages of networks are much higher than those of proteins, implying that different disorder classes usually influence each other by means of their interacting neighbors. The metabolic, muscular and hematologic proteins are related with most tissues/cells, and cancer proteins are closely associated with the disorders in immune cells.

CONCLUSION

This paper provided novel methods to investigate proteome-wide disease proteins and their interacting networks in order to understand different disease's associations.

Computational Prediction of DrugTarget Interactions Using Chemical, Biological, and Network Features

Drugtarget interactions (DTIs) are central to current drug discovery processes. Efforts have been devoted to the development of methodology for predicting DTIs and drugtarget interaction networks. Most existing methods mainly focus on the application of information about drug or protein structure features. In the present work, we proposed a computational method for DTI prediction by combining the information from chemical, biological and network properties. The method was developed based on a learning algorithm-random forest (RF) combined with integrated features for predicting DTIs. Four classes of drugtarget interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models. The RF models gave prediction accuracy of 93.52 %, 94.84 %, 89.68 % and 84.72 % for four pharmaceutically useful datasets, respectively. The prediction ability of our approach is comparative to or even better than that of other DTI prediction methods. These comparative results demonstrated the relevance of the network topology as source of information for predicting DTIs. Further analysis confirmed that among our top ranked predictions of DTIs, several DTIs are supported by databases, while the others represent novel potential DTIs. We believe that our proposed approach can help to limit the search space of DTIs and provide a new way towards repositioning old drugs and identifying targets.