Multi-view Graph Embedding with Hub Detection for Brain Network Analysis

Multi-view graph embedding has become a widely studied problem in the area of graph learning. Most of the existing works on multi-view graph embedding aim to find a shared common node embedding across all the views of the graph by combining the different views in a specific way. Hub detection, as another essential topic in graph mining has also drawn extensive attentions in recent years, especially in the context of brain network analysis. Both the graph embedding and hub detection relate to the node clustering structure of graphs. The multi-view graph embedding usually implies the node clustering structure of the graph based on the multiple views, while the hubs are the boundary-spanning nodes across different node clusters in the graph and thus may potentially influence the clustering structure of the graph. However, none of the existing works in multi-view graph embedding considered the hubs when learning the multi-view embeddings. In this paper, we propose to incorporate the hub detection task into the multi-view graph embedding framework so that the two tasks could benefit each other. Specifically, we propose an auto-weighted framework of Multi-view Graph Embedding with Hub Detection (MVGE-HD) for brain network analysis. The MVGE-HD framework learns a unified graph embedding across all the views while reducing the potential influence of the hubs on blurring the boundaries between node clusters in the graph, thus leading to a clear and discriminative node clustering structure for the graph. We apply MVGE-HD on two real multi-view brain network datasets (i.e., HIV and Bipolar). The experimental results demonstrate the superior performance of the proposed framework in brain network analysis for clinical investigation and application

Memory-Augmented Graph Neural Networks: A Neuroscience Perspective

Graph neural networks (GNNs) have been extensively used for many domains where data are represented as graphs, including social networks, recommender systems, biology, chemistry, etc. Recently, the expressive power of GNNs has drawn much interest. It has been shown that, despite the promising empirical results achieved by GNNs for many applications, there are some limitations in GNNs that hinder their performance for some tasks. For example, since GNNs update node features mainly based on local information, they have limited expressive power in capturing long-range dependencies among nodes in graphs. To address some of the limitations of GNNs, several recent works started to explore augmenting GNNs with memory for improving their expressive power in the relevant tasks. In this paper, we provide a comprehensive review of the existing literature of memory-augmented GNNs. We review these works through the lens of psychology and neuroscience, which has established multiple memory systems and mechanisms in biological brains. We propose a taxonomy of the memory GNN works, as well as a set of criteria for comparing the memory mechanisms. We also provide critical discussions on the limitations of these works. Finally, we discuss the challenges and future directions for this area

Daucosterol pretreatment ameliorates myocardial ischemia reperfusion injury via ROS-mediated NLRP3 inflammasome activation

Purpose: To determine the involvement of NLRP3 signaling pathway in the preventive role of daucosterol in acute myocardial infarction (AMI).Methods: H9C2 cells were pretreated with daucosterol before hypoxia/reoxygenation (HR) injury. Myocardial ischemia reperfusion (IR) was established in male SD rats, followed by reperfusion. Myocardial infarct size was measured. The serum levels of creatine kinase (CK), lactate  dehydrogenase (LDH), total superoxide dismutase (T-SOD), and malondialdehyde (MDA) were determined using commercial kits. NLRP3 inflammasome activation was assessed by western blotting.Results: Myocardial infarct size was smaller after IR injury in rats pretreated with daucosterol (10 and 50 mg/kg) than that pretreated with daucosterol (0 and 1 mg/kg). The increase in LDH, CK, and MDA levels after IR injury was reduced following daucosterol pretreatment. Reactive oxygen species (ROS) production increased, whereas T-SOD activity decreased after IR injury. These changes were prevented by pretreatment of daucosterol (10 and 50 mg/kg). Protein expression of NLRP3 inflammasome increased after IR injury in H9C2 cells while pretreatment with daucosterol inhibited the upregulation of NLRP3 inflammasome.Conclusion: The cardioprotective effect of daucosterol pretreatment appears to be mediated via the inactivation of ROS-related NLRP3 inflammasome, suggesting that daucosteol might be a potential therapeutic drug for AMI. Keywords: Daucosterol, Myocardial ischemia, Reperfusion injury, Reactive oxygen species, NLRP3 inflammasom

Robust Ranking Explanations

Robust explanations of machine learning models are critical to establish human trust in the models. Due to limited cognition capability, most humans can only interpret the top few salient features. It is critical to make top salient features robust to adversarial attacks, especially those against the more vulnerable gradient-based explanations. Existing defense measures robustness using $\ell_p$-norms, which have weaker protection power. We define explanation thickness for measuring salient features ranking stability, and derive tractable surrogate bounds of the thickness to design the \textit{R2ET} algorithm to efficiently maximize the thickness and anchor top salient features. Theoretically, we prove a connection between R2ET and adversarial training. Experiments with a wide spectrum of network architectures and data modalities, including brain networks, demonstrate that R2ET attains higher explanation robustness under stealthy attacks while retaining accuracy.Comment: Accepted to IMLH (Interpretable ML in Healthcare) workshop at ICML 2023. arXiv admin note: substantial text overlap with arXiv:2212.1410

Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model

Recently brain networks have been widely adopted to study brain dynamics, brain development and brain diseases. Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. However, current graph learning techniques have several issues on brain network mining. Firstly, most current graph learning models are designed for unsigned graph, which hinders the analysis of many signed network data (e.g., brain functional networks). Meanwhile, the insufficiency of brain network data limits the model performance on clinical phenotypes predictions. Moreover, few of current graph learning model is interpretable, which may not be capable to provide biological insights for model outcomes. Here, we propose an interpretable hierarchical signed graph representation learning model to extract graph-level representations from brain functional networks, which can be used for different prediction tasks. In order to further improve the model performance, we also propose a new strategy to augment functional brain network data for contrastive learning. We evaluate this framework on different classification and regression tasks using the data from HCP and OASIS. Our results from extensive experiments demonstrate the superiority of the proposed model compared to several state-of-the-art techniques. Additionally, we use graph saliency maps, derived from these prediction tasks, to demonstrate detection and interpretation of phenotypic biomarkers