In recent years, Knowledge Graphs (KG) have been widely used in many artificial intelligence tasks. Knowledge graphs represent entities and their relationships using triplets consisting of a head entity, a relation, and a tail entity. For example, the triplet (h = Paris, r = capital_of, t = France) represents a common-sense fact about the real worl...
Knowledge Graph (KG) embedding technology is an important research topic in the field of artificial intelligence, mainly used for knowledge acquisition and extension of knowledge graphs. In recent years, although many graph embedding methods have been proposed, these methods typically focus only on the semantic information of the knowledge graph, i...
Graph-Based Conditional Generative Adversarial Network for Generating Synthetic Functional Brain Networks to Diagnose Major Depressive Disorder Research Background: Major Depressive Disorder (MDD) is a widespread mental disorder that affects millions of people’s lives and poses a significant threat to global health. Studies have shown that function...
Parkinson’s disease (PD) is a common neurodegenerative disorder characterized mainly by resting tremor, bradykinesia, and rigidity. Deep Brain Stimulation (DBS) has been widely used to treat the motor symptoms of PD (Krauss et al., 2021). However, DBS treatment also has significant side effects, most of which are caused by the extension of stimulat...
Research Background and Purpose Epilepsy is a spontaneous and severe neurological disorder characterized by recurrent seizures, affecting approximately 50 million people worldwide [1]. Despite recent advances in anti-seizure medications (ASMs), drug-resistant epilepsy (DRE) still affects 20% to 30% of epilepsy patients [1-3]. DRE patients face sign...
Brain Functional Network for Anxiety Analysis and Detection Based on Improved Ensemble Empirical Mode Decomposition Academic Background and Research Objectives With the increasing stress of modern life, anxiety, a common neurological disorder, has become an urgent issue in global public health. Anxiety not only manifests as mental disorders but als...
The Role of EEG Microstates in Predicting the Therapeutic Outcomes of Oxcarbazepine in Newly Diagnosed Focal Epilepsy Patients Introduction Background Focal epilepsy is the most common type of epilepsy, accounting for about 60% of all epilepsy cases. The selection of antiepileptic drugs (AEDs) varies depending on the type of epilepsy. In the treatm...
Multi-Level Feature Exploration and Fusion Network for Prediction of IDH Status in MRI Background Glioma is the most common malignant primary brain tumor in adults. According to the 2021 World Health Organization (WHO) classification of tumors, genotype plays a significant role in the classification of tumor subtypes, especially the isocitrate dehy...
In modern medical diagnostics and clinical research, Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) technology provides significant information regarding tissue pathophysiology. By fitting a Tracer-Kinetic (TK) model, pharmacokinetic (PK) parameters can be extracted from time-series MRI signals. However, these estimated PK parameter...
Classification of 3D MRI Gliomas and Alzheimer’s Disease Based on the Siamese-Transport Domain Adaptation Framework Background In computer-aided diagnosis, 3D magnetic resonance imaging (MRI) screening plays a vital role in the early diagnosis of various brain diseases, effectively preventing the deterioration of the condition. Glioma is a common m...