CI-GNN: A Granger Causality-Inspired Graph Neural Network for Interpretable Brain Network-Based Psychiatric Diagnosis

There is a recent trend to leverage the power of graph neural networks (GNNs) for brain-network based psychiatric diagnosis, which,in turn, also motivates an urgent need for psychiatrists to fully understand the decision behavior of the used GNNs. However, most of the existing GNN explainers are either post-hoc in which another interpretive model needs to be created to explain a well-trained GNN, or do not consider the causal relationship between the extracted explanation and the decision, such that the explanation itself contains spurious correlations and suffers from weak faithfulness. In this work, we propose a granger causality-inspired graph neural network (CI-GNN), a built-in interpretable model that is able to identify the most influential subgraph (i.e., functional connectivity within brain regions) that is causally related to the decision (e.g., major depressive disorder patients or healthy controls), without the training of an auxillary interpretive network. CI-GNN learns disentangled subgraph-level representations {\alpha} and \b{eta} that encode, respectively, the causal and noncausal aspects of original graph under a graph variational autoencoder framework, regularized by a conditional mutual information (CMI) constraint. We theoretically justify the validity of the CMI regulation in capturing the causal relationship. We also empirically evaluate the performance of CI-GNN against three baseline GNNs and four state-of-the-art GNN explainers on synthetic data and three large-scale brain disease datasets. We observe that CI-GNN achieves the best performance in a wide range of metrics and provides more reliable and concise explanations which have clinical evidence.Comment: 45 pages, 13 figure

Towards a More Stable and General Subgraph Information Bottleneck

Graph Neural Networks (GNNs) have been widely applied to graph-structured data. However, the lack of interpretability impedes its practical deployment especially in high-risk areas such as medical diagnosis. Recently, the Information Bottleneck (IB) principle has been extended to GNNs to identify a compact subgraph that is most informative to class labels, which significantly improves the interpretability on decision. However, existing Graph Information Bottleneck (GIB) models are either unstable during the training (due to the difficulty of mutual information estimation) or only focus on a special kind of graph (e.g., brain networks) that suffer from poor generalization to general graph datasets with varying graph sizes. In this work, we extend the recently developed Brain Information Bottleneck (BrainIB) to general graphs by introducing matrix-based Rényi's α-order mutual information to stablize the training; and by designing a novel mask strategy to deal with varying graph sizes such that the new method can also be used for social networks, molecules, etc. Extensive experiments on different types of graph datasets demonstrate the superior stability and generality of our model.</p

Large-scale effective connectivity analysis reveals the existence of two mutual inhibitory systems in patients with major depression

It is posited that cognitive and affective dysfunction in patients with major depression disorder (MDD) may be caused by dysfunctional signal propagation in the brain. By leveraging dynamic causal modeling, we investigated large-scale directed signal propagation (effective connectivity) among distributed large-scale brain networks with 43 MDD patients and 56 healthy controls. The results revealed the existence of two mutual inhibitory systems: the anterior default mode network, auditory network, sensorimotor network, salience network and visual networks formed an “emotional” brain, while the posterior default mode network, central executive networks, cerebellum and dorsal attention network formed a “rational brain”. These two networks exhibited excitatory intra-system connectivity and inhibitory inter-system connectivity. Patients were characterized by potentiated intra-system connections within the “emotional/sensory brain”, as well as over-inhibition of the “rational brain” by the “emotional/sensory brain”. The hierarchical architecture of the large-scale effective connectivity networks was then analyzed using a PageRank algorithm which revealed a shift of the controlling role of the “rational brain” to the “emotional/sensory brain” in the patients. These findings inform basic organization of distributed large-scale brain networks and furnish a better characterization of the neural mechanisms of depression, which may facilitate effective treatment

Current limiting simulation of magneto-biased superconducting fault current limiter (SFCL) applied in 66kV/10kV power substation in China

The short-circuit fault currents in the large capacity transmission lines pose a challenge to the stability and safety of power system. Magneto-biased superconducting fault current limiter (MBSFCL) is with the advantages of two-stage current limiting, self-triggering and fast recovery so that it can effectively reduce the short-circuit fault current when applied to the urban power system. This paper analyzes the operation principle of MBSFCL and establishes a package type magneto-thermal coupled simulation model of MBSFCL based on MATLAB/SIMULINK. A power system model of a 66 kV/10 kV Zhang Tai Zi power substation which is in Liaoning province in China has been established and the grid-connected simulation applied MBSFCL has been achieved. Moreover, the grid-connected test is carried out, and the results of fault current, quench resistance and temperature change are obtained. The comparison of results between simulation and test verifies the accuracy of the simulation model and the application feasibility of MBSFCL

Enhancing the database of OTRmail extension for thunderbird

The Internet in the 21st century has become an indispensable part of our lives and this has led to the proliferation of Electronic mail (Email). Despite its conveniences, Email faces several problems such as security issues and this has been addressed by various Email security solutions. However, these solutions use long-lived encryption keys, which limit their usage. Arising from the need to overcome this limitation is the OTRmail. This Email security solution is based on the concept of Off-The-Record (OTR) which introduces the idea of perfect forward secrecy, repudiability and the usage of short-lived secret key.Although a raw version of the OTRmail engine has been developed, much work is required to improve it, especially its database system. This is due to the introduction of new features for its users in the OTRmail which requires the storage and retrieval of new key attributes. Thus, the focus of this project is to improve the OTRmail’s database by researching on suitable database systems, developing the chosen system, integrating it into OTRmail and the development of the User Interface (UI) to gather user’s inputs.Bachelor of Engineering (Computer Engineering

An Adaptive LDA Optimal Topic Number Selection Method in News Topic Identification

Nowadays, news text information is exploding, and people need more and more heterogeneous news content. Therefore, news text topic identification is needed to help viewers quickly and accurately screen and filter news related to their interests to save time and energy. The Latent Dirichlet Allocation (LDA) model is the most commonly used method for text topic identification. The optimal number of topics must be specified in advance when using the LDA model to extract topics in previous studies. However, selecting the too-large or the too-small number of topics significantly impacts the final results of LDA topic models, directly determining the quality of topic extraction. Moreover, the news text datasets from social media are very time-sensitive, and the combination of temporal and semantic modeling has not been considered in past studies of news topic identification. This paper proposes an adaptive optimal topic number determination method for fusing semantic and temporal information in news datasets to address the existing problems. Semantic and temporal are first extracted in this method as two different views. Then, density peak clustering of multi-view information fusion is performed based on the two obtained feature vectors. The clustering results are used as the final optimal number of topics. To demonstrate the effectiveness of the proposed method, this paper compares the performance of four traditional methods for determining the optimal number of topics with the performance of this paper&#x2019;s method on public datasets. The results show that the optimal number of topics considering semantic and temporal factors is significantly better than the other four traditional methods regarding F-value, PMI scores, and MI scores. It performs well in other indicators as well. The above experimental results show that the method proposed in this paper combines the temporal and semantic of news data to determine the optimal number of topics of news text, which can improve the accuracy of selecting the optimal number of topics in the LDA model and the effectiveness of the topic identification of news text to some extent. It can help viewers better understand and utilize the massive news text information. In addition, the method also broadens the idea of identifying and mining unique datasets from multiple perspectives

Mean first passage time of preferential random walks on complex networks with applications

This paper investigates, both theoretically and numerically, preferential random walks (PRW) on weighted complex networks. By using two different analytical methods, two exact expressions are derived for the mean first passage time (MFPT) between two nodes. On one hand, the MFPT is got explicitly in terms of the eigenvalues and eigenvectors of a matrix associated with the transition matrix of PRW. On the other hand, the center-product-degree (CPD) is introduced as one measure of node strength and it plays a main role in determining the scaling of the MFPT for the PRW. Comparative studies are also performed on PRW and simple random walks (SRW). Numerical simulations of random walks on paradigmatic network models confirm analytical predictions and deepen discussions in different aspects. The work may provide a comprehensive approach for exploring random walks on complex networks, especially biased random walks, which may also help to better understand and tackle some practical problems such as search and routing on networks.Published versio

BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping

Converging evidence increasingly suggests that psychiatric disorders, such as major depressive disorder (MDD) and autism spectrum disorder (ASD), are not unitary diseases, but rather heterogeneous syndromes that involve diverse, co-occurring symptoms and divergent responses to treatment. This clinical heterogeneity has hindered the progress of precision diagnosis and treatment effectiveness in psychiatric disorders. In this study, we propose BPI-GNN, a new interpretable graph neural network (GNN) framework for analyzing functional magnetic resonance images (fMRI), by leveraging the famed prototype learning. In addition, we introduce a novel generation process of prototype subgraph to discover essential edges of distinct prototypes and employ total correlation (TC) to ensure the independence of distinct prototype subgraph patterns. BPI-GNN can effectively discriminate psychiatric patients and healthy controls (HC), and identify biological meaningful subtypes of psychiatric disorders. We evaluate the performance of BPI-GNN against 11 popular brain network classification methods on three psychiatric datasets and observe that our BPI-GNN always achieves the highest diagnosis accuracy. More importantly, we examine differences in clinical symptom profiles and gene expression profiles among identified subtypes and observe that our identified brain-based subtypes have the clinical relevance. It also discovers the subtype biomarkers that align with current neuro-scientific knowledge

BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck

Developing a new diagnostic models based on the underlying biological mechanisms rather than subjective symptoms for psychiatric disorders is an emerging consensus. Recently, machine learning-based classifiers using functional connectivity (FC) for psychiatric disorders and healthy controls are developed to identify brain markers. However, existing machine learningbased diagnostic models are prone to over-fitting (due to insufficient training samples) and perform poorly in new test environment. Furthermore, it is difficult to obtain explainable and reliable brain biomarkers elucidating the underlying diagnostic decisions. These issues hinder their possible clinical applications. In this work, we propose BrainIB, a new graph neural network (GNN) framework to analyze functional magnetic resonance images (fMRI), by leveraging the famed Information Bottleneck (IB) principle. BrainIB is able to identify the most informative regions in the brain (i.e., subgraph) and generalizes well to unseen data. We evaluate the performance of BrainIB against 6 popular brain network classification methods on two multi-site, largescale datasets and observe that our BrainIB always achieves the highest diagnosis accuracy. It also discovers the subgraph biomarkers which are consistent to clinical and neuroimaging findings.Comment: 12 pages, 4 figure