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 …
Linear graph convolutional model for diagnosing brain disorders
Z Rakhimberdina, T Murata - … Networks and Their Applications VIII: Volume …, 2020 - Springer
Deep learning models find an increasing application in the diagnosis of brain disorders.
Designed for large scale datasets, deep neural networks (DNNs) achieve state-of-the-art …
Designed for large scale datasets, deep neural networks (DNNs) achieve state-of-the-art …
Benchmarking graph neural networks for fMRI analysis
Graph Neural Networks (GNNs) have emerged as a powerful tool to learn from graph-
structured data. A paramount example of such data is the brain, which operates as a …
structured data. A paramount example of such data is the brain, which operates as a …
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 …
Joint embedding of structural and functional brain networks with graph neural networks for mental illness diagnosis
Multimodal brain networks characterize complex connectivities among different brain
regions from both structural and functional aspects and provide a new means for mental …
regions from both structural and functional aspects and provide a new means for mental …
Hi-GCN: A hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction
Purpose Recently, brain connectivity networks have been used for the classification of
neurological disorder, such as Autism Spectrum Disorders (ASD) or Alzheimer's disease …
neurological disorder, such as Autism Spectrum Disorders (ASD) or Alzheimer's disease …
Graph neural network and machine learning analysis of functional neuroimaging for understanding schizophrenia
Background Graph representational learning can detect topological patterns by leveraging
both the network structure as well as nodal features. The basis of our exploration involves …
both the network structure as well as nodal features. The basis of our exploration involves …
The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook
S Zhang, J Yang, Y Zhang, J Zhong, W Hu, C Li… - Brain Sciences, 2023 - mdpi.com
Neurological disorders (NDs), such as Alzheimer's disease, have been a threat to human
health all over the world. It is of great importance to diagnose ND through combining artificial …
health all over the world. It is of great importance to diagnose ND through combining artificial …
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 …
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 …