[HTML][HTML] A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD
The pathological mechanism of attention deficit hyperactivity disorder (ADHD) is
incompletely specified, which leads to difficulty in precise diagnosis. Functional magnetic …
incompletely specified, which leads to difficulty in precise diagnosis. Functional magnetic …
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 …
Graph convolutional network for fMRI analysis based on connectivity neighborhood
There have been successful applications of deep learning to functional magnetic resonance
imaging (fMRI), where fMRI data were mostly considered to be structured grids, and spatial …
imaging (fMRI), where fMRI data were mostly considered to be structured grids, and spatial …
M-gcn: A multimodal graph convolutional network to integrate functional and structural connectomics data to predict multidimensional phenotypic characterizations
We propose a multimodal graph convolutional network (M-GCN) that integrates resting-state
fMRI connectivity and diffusion tensor imaging tractography to predict phenotypic measures …
fMRI connectivity and diffusion tensor imaging tractography to predict phenotypic measures …
MVS-GCN: A prior brain structure learning-guided multi-view graph convolution network for autism spectrum disorder diagnosis
Purpose Recently, functional brain networks (FBN) have been used for the classification of
neurological disorders, such as Autism Spectrum Disorders (ASD). Neurological disorder …
neurological disorders, such as Autism Spectrum Disorders (ASD). Neurological disorder …
Neurograph: Benchmarks for graph machine learning in brain connectomics
Abstract Machine learning provides a valuable tool for analyzing high-dimensional
functional neuroimaging data, and is proving effective in predicting various neurological …
functional neuroimaging data, and is proving effective in predicting various neurological …
Temporal-adaptive graph convolutional network for automated identification of major depressive disorder using resting-state fMRI
Extensive studies focus on analyzing human brain functional connectivity from a network
perspective, in which each network contains complex graph structures. Based on resting …
perspective, in which each network contains complex graph structures. Based on resting …
Graph neural networks in network neuroscience
A Bessadok, MA Mahjoub… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Noninvasive medical neuroimaging has yielded many discoveries about the brain
connectivity. Several substantial techniques mapping morphological, structural and …
connectivity. Several substantial techniques mapping morphological, structural and …
Triplet graph convolutional network for multi-scale analysis of functional connectivity using functional MRI
Brain functional connectivity (FC) derived from resting-state functional MRI (rs-fMRI) data
has become a powerful approach to measure and map brain activity. Using fMRI data, graph …
has become a powerful approach to measure and map brain activity. Using fMRI data, graph …