Deep representation learning for multimodal brain networks
Applying network science approaches to investigate the functions and anatomy of the
human brain is prevalent in modern medical imaging analysis. Due to the complex network …
human brain is prevalent in modern medical imaging analysis. Due to the complex network …
MAMF-GCN: Multi-scale adaptive multi-channel fusion deep graph convolutional network for predicting mental disorder
J Pan, H Lin, Y Dong, Y Wang, Y Ji - Computers in biology and medicine, 2022 - Elsevier
Purpose Existing diagnoses of mental disorders rely on symptoms, patient descriptions, and
scales, which are not objective enough. We attempt to explore an objective diagnostic …
scales, which are not objective enough. We attempt to explore an objective diagnostic …
Joint graph convolution for analyzing brain structural and functional connectome
Abstract The white-matter (micro-) structural architecture of the brain promotes synchrony
among neuronal populations, giving rise to richly patterned functional connections. A …
among neuronal populations, giving rise to richly patterned functional connections. A …
An enhanced multi-modal brain graph network for classifying neuropsychiatric disorders
It has been proven that neuropsychiatric disorders (NDs) can be associated with both
structures and functions of brain regions. Thus, data about structures and functions could be …
structures and functions of brain regions. Thus, data about structures and functions could be …
Ptgb: Pre-train graph neural networks for brain network analysis
The human brain is the central hub of the neurobiological system, controlling behavior and
cognition in complex ways. Recent advances in neuroscience and neuroimaging analysis …
cognition in complex ways. Recent advances in neuroscience and neuroimaging analysis …
Collaborative learning of graph generation, clustering and classification for brain networks diagnosis
Purpose: Accurate diagnosis of autism spectrum disorder (ASD) plays a key role in
improving the condition and quality of life for patients. In this study, we mainly focus on ASD …
improving the condition and quality of life for patients. In this study, we mainly focus on ASD …
TE-HI-GCN: An ensemble of transfer hierarchical graph convolutional networks for disorder diagnosis
Accurate diagnosis of psychiatric disorders plays a critical role in improving the quality of life
for patients and potentially supports the development of new treatments. Graph …
for patients and potentially supports the development of new treatments. Graph …
Braingb: a benchmark for brain network analysis with graph neural networks
Mapping the connectome of the human brain using structural or functional connectivity has
become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph …
become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph …
Spatio-temporal modeling of connectome-scale brain network interactions via time-evolving graphs
Many recent literature studies have revealed interesting dynamics patterns of functional
brain networks derived from fMRI data. However, it has been rarely explored how functional …
brain networks derived from fMRI data. However, it has been rarely explored how functional …
Multi-scale dynamic graph learning for brain disorder detection with functional MRI
Y Ma, Q Wang, L Cao, L Li, C Zhang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used in the
detection of brain disorders such as autism spectrum disorder based on various …
detection of brain disorders such as autism spectrum disorder based on various …