Spectral Graph Neural Network-Based Multi-Atlas Brain Network Fusion for Major Depressive Disorder Diagnosis.

Major Depressive Disorder (MDD) imposes a substantial burden within the healthcare domain, impacting millions of individuals worldwide. Functional Magnetic Resonance Imaging (fMRI) has emerged as a promising tool for the objective diagnosis of MDD, enabling the investigation of functional connectivity patterns in the brain associated with this disorder. However, most existing methods focus on a single brain atlas, which limits their ability to capture the complex, multi-scale nature of functional brain networks. To address these limitations, we propose a novel multi-atlas fusion method that incorporates early and late fusion in a unified framework. Our method introduces the concept of the holistic Functional Connectivity Network (FCN), which captures both intra-atlas relationships within individual atlases and inter-regional relationships between atlases with different brain parcellation scales. This comprehensive representation enables the identification of potential disease-related patterns associated with MDD in the early stage of our framework. Moreover, by decoding the holistic FCN from various perspectives through multiple spectral Graph Convolutional Neural Networks and fusing their results with decision-level ensembles, we further improve the performance of MDD diagnosis. Our approach is easily implemented with minimal modifications to existing model structures and demonstrates a robust performance across different baseline models. Our method, evaluated on public resting-state fMRI datasets, surpasses the current multi-atlas fusion methods, enhancing the accuracy of MDD diagnosis. The proposed novel multi-atlas fusion framework provides a more reliable MDD diagnostic technique. Experimental results show our approach outperforms both single- and multi-atlas-based methods, demonstrating its effectiveness in advancing MDD diagnosis.

Graph-Based Conditional Generative Adversarial Networks for Major Depressive Disorder Diagnosis With Synthetic Functional Brain Network Generation.

Major Depressive Disorder (MDD) is a pervasive disorder affecting millions of individuals, presenting a significant global health concern. Functional connectivity (FC) derived from resting-state functional Magnetic Resonance Imaging (rs-fMRI) serves as a crucial tool in revealing functional connectivity patterns associated with MDD, playing an essential role in precise diagnosis. However, the limited data availability of FC poses challenges for robust MDD diagnosis. To tackle this, some studies have employed Deep Neural Networks (DNN) architectures to construct Generative Adversarial Networks (GAN) for synthetic FC generation, but this tends to overlook the inherent topology characteristics of FC. To overcome this challenge, we propose a novel Graph Convolutional Networks (GCN)-based Conditional GAN with Class-Aware Discriminator (GC-GAN). GC-GAN utilizes GCN in both the generator and discriminator to capture intricate FC patterns among brain regions, and the class-aware discriminator ensures the diversity and quality of the generated synthetic FC. Additionally, we introduce a topology refinement technique to enhance MDD diagnosis performance by optimizing the topology using the augmented FC dataset. Our framework was evaluated on publicly available rs-fMRI datasets, and the results demonstrate that GC-GAN outperforms existing methods. This indicates the superior potential of GCN in capturing intricate topology characteristics and generating high-fidelity synthetic FC, thus contributing to a more robust MDD diagnosis.

The possible origin of human adenovirus type 3: Evidence of natural genetic recombination between human and simian adenovirus.

We determined a complete genome sequence of the Korean field strain, KUMC-62, of human adenovirus type 3 (HAdV-3) and performed comparative genome analyses. Interestingly HAdV-3 has a distinct genomic sequence for the fiber CDS region on average 62.46% of nucleotide sequence identity to other types of HAdV-B1, while remaining genomic region of HAdV-3 is very similar (on average 95.71% of nucleotide sequence identity) to other types of HAdV-B1. The blast results showed that the fiber CDS region of HAdV-3 exhibited the highest nucleotide sequence identity with that of simian adenovirus type 32 (SAdV-32), except other strains of HAdV-3. In the Simplot analysis, a potential recombination event was detected between HAdV-7 and SAdV-32, which might have created HAdV-3 in the past. These findings suggest that HAdV-3 highly likely was created by a natural inter-species recombination event between human and non-human primate AdVs.