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 …
A mutual multi-scale triplet graph convolutional network for classification of brain disorders using functional or structural connectivity
Brain connectivity alterations associated with mental disorders have been widely reported in
both functional MRI (fMRI) and diffusion MRI (dMRI). However, extracting useful information …
both functional MRI (fMRI) and diffusion MRI (dMRI). However, extracting useful information …
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 …
Braingnn: Interpretable brain graph neural network for fmri analysis
Understanding which brain regions are related to a specific neurological disorder or
cognitive stimuli has been an important area of neuroimaging research. We propose …
cognitive stimuli has been an important area of neuroimaging research. We propose …
Discriminative analysis of schizophrenia patients using graph convolutional networks: A combined multimodal MRI and connectomics analysis
X Chen, P Ke, Y Huang, J Zhou, H Li, R Peng… - Frontiers in …, 2023 - frontiersin.org
Introduction Recent studies in human brain connectomics with multimodal magnetic
resonance imaging (MRI) data have widely reported abnormalities in brain structure …
resonance imaging (MRI) data have widely reported abnormalities in brain structure …
Multi-graph attention networks with bilinear convolution for diagnosis of schizophrenia
The explorations of brain functional connectivity (FC) network using resting-state functional
magnetic resonance imaging (rs-fMRI) can provide crucial insights into discriminative …
magnetic resonance imaging (rs-fMRI) can provide crucial insights into discriminative …
Graph neural network for interpreting task-fmri biomarkers
Finding the biomarkers associated with ASD is helpful for understanding the underlying
roots of the disorder and can lead to earlier diagnosis and more targeted treatment. A …
roots of the disorder and can lead to earlier diagnosis and more targeted treatment. A …
Classification of brain disorders in rs-fMRI via local-to-global graph neural networks
H Zhang, R Song, L Wang, L Zhang… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
Recently, functional brain network has been used for the classification of brain disorders,
such as Autism Spectrum Disorder (ASD) and Alzheimer's disease (AD). Existing methods …
such as Autism Spectrum Disorder (ASD) and Alzheimer's disease (AD). Existing methods …
Pooling regularized graph neural network for fmri biomarker analysis
Understanding how certain brain regions relate to a specific neurological disorder has been
an important area of neuroimaging research. A promising approach to identify the salient …
an important area of neuroimaging research. A promising approach to identify the salient …
Graph autoencoders for embedding learning in brain networks and major depressive disorder identification
Brain functional connectivity (FC) networks inferred from functional magnetic resonance
imaging (fMRI) have shown altered or aberrant brain functional connectome in various …
imaging (fMRI) have shown altered or aberrant brain functional connectome in various …