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Approaches to the Use of Graph Theory to Study the Human EEG in Health and Cerebral Pathology
The information content of EEG recordings, which are widely used and important for assessing the functional activity of the brain, is significantly...
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Topological Organization of the Brain Network in Patients with Primary Angle-closure Glaucoma Through Graph Theory Analysis
Primary angle-closure glaucoma (PACG) is a sight-threatening eye condition that leads to irreversible blindness. While past neuroimaging research has...
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Functional Connectivity Alterations in Patients with Post-stroke Epilepsy Based on Source-level EEG and Graph Theory
We investigated the differences in functional connectivity based on the source-level electroencephalography (EEG) analysis between stroke patients...
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Potential biomarker for early detection of ADHD using phase-based brain connectivity and graph theory
This research investigates an efficient strategy for early detection and intervention of attention-deficit hyperactivity disorder (ADHD) in children....
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New results of partially total fuzzy graph
ObjectiveThe study of total fuzzy graphs in all cases is crucial for the development of both theories and applications of the graph theory. Without...
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Graph neural network and machine learning analysis of functional neuroimaging for understanding schizophrenia
BackgroundGraph representational learning can detect topological patterns by leveraging both the network structure as well as nodal features. The...
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Semi-supervised bipartite graph construction with active EEG sample selection for emotion recognition
AbstractElectroencephalogram (EEG) signals are derived from the central nervous system and inherently difficult to camouflage, leading to the recent...
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Dynamical graph neural network with attention mechanism for epilepsy detection using single channel EEG
AbstractEpilepsy is a chronic brain disease, and identifying seizures based on electroencephalogram (EEG) signals would be conducive to implement...
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Graph features based classification of bronchial and pleural rub sound signals: the potential of complex network unwrapped
The study presents a novel technique for lung auscultation based on graph theory, emphasizing the potential of graph parameters in distinguishing...
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Automatic segmentation of layers in chorio-retinal complex using Graph-based method for ultra-speed 1.7 MHz wide field swept source FDML optical coherence tomography
The posterior segment of the human eye complex contains two discrete microstructure and vasculature network systems, namely, the retina and choroid....
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Graph representation learning in biomedicine and healthcare
Networks—or graphs—are universal descriptors of systems of interacting elements. In biomedicine and healthcare, they can represent, for example,...
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Dynamic PET images denoising using spectral graph wavelet transform
AbstractPositron emission tomography (PET) is a non-invasive molecular imaging method for quantitative observation of physiological and biochemical...
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Changes of brain functional network in Alzheimer’s disease and frontotemporal dementia: a graph-theoretic analysis
BackgroundAlzheimer’s disease (AD) and frontotemporal dementia (FTD) are the two most common neurodegenerative dementias, presenting with similar...
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Novel channel selection model based on graph convolutional network for motor imagery
Multi-channel electroencephalography (EEG) is used to capture features associated with motor imagery (MI) based brain-computer interface (BCI) with a...
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Cocrystal Prediction of Nifedipine Based on the Graph Neural Network and Molecular Electrostatic Potential Surface
Nifedipine (NIF) is a dihydropyridine calcium channel blocker primarily used to treat conditions such as hypertension and angina. However, its low...
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Multimodal and hemispheric graph-theoretical brain network predictors of learning efficacy for frontal alpha asymmetry neurofeedback
EEG neurofeedback using frontal alpha asymmetry (FAA) has been widely used for emotion regulation, but its effectiveness is controversial. Studies...
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Graph Kernel Learning for Predictive Toxicity Models
Graph-driven techniques have been widely used in chemoinformatics and bioinformatics. It is of a great beneficial to develop toxicity prediction...
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A robust and stable gene selection algorithm based on graph theory and machine learning
BackgroundNowadays we are observing an explosion of gene expression data with phenotypes. It enables us to accurately identify genes responsible for...
