[HTML][HTML] A survey of deep learning for alzheimer's disease
Q Zhou, J Wang, X Yu, S Wang, Y Zhang - Machine Learning and …, 2023 - mdpi.com
Alzheimer's and related diseases are significant health issues of this era. The
interdisciplinary use of deep learning in this field has shown great promise and gathered …
interdisciplinary use of deep learning in this field has shown great promise and gathered …
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 …
Multi-scale spatio-temporal fusion with adaptive brain topology learning for fMRI based neural decoding
Z Li, Q Li, Z Zhu, Z Hu, X Wu - IEEE Journal of Biomedical and …, 2023 - ieeexplore.ieee.org
Neural decoding aims to extract information from neurons' activities to reveal how the brain
functions. Due to the inherent spatial and temporal characteristics of brain signals, spatio …
functions. Due to the inherent spatial and temporal characteristics of brain signals, spatio …
Classification of autism based on short-term spontaneous hemodynamic fluctuations using an adaptive graph neural network
Y Zhu, L Xu, J Yu - Journal of Neuroscience Methods, 2023 - Elsevier
Background: Short-term spontaneous hemodynamic fluctuations were collected by the
functional near-infrared spectroscopy (fNIRS) system to classify children with autism …
functional near-infrared spectroscopy (fNIRS) system to classify children with autism …
Dynamic adaptive spatio–temporal graph network for COVID‐19 forecasting
Appropriately characterising the mixed space–time relations of the contagion process
caused by hybrid space and time factors remains the primary challenge in COVID‐19 …
caused by hybrid space and time factors remains the primary challenge in COVID‐19 …
Brainnet with connectivity attention for individualized predictions based on multi-facet connections extracted from resting-state fmri data
H Ma, F Wu, Y Guan, L Xu, J Liu, L Tian - Cognitive Computation, 2023 - Springer
Resting-state functional magnetic resonance imaging (RS-fMRI) has great potential for
clinical applications. This study aimed to promote the performance of RS-fMRI-based …
clinical applications. This study aimed to promote the performance of RS-fMRI-based …
Cross-domain identification of multisite major depressive disorder using end-to-end brain dynamic attention network
X Yuan, M Chen, P Ding, A Gan, A Gong… - … on Neural Systems …, 2023 - ieeexplore.ieee.org
Establishing objective and quantitative imaging markers at individual level can assist in
accurate diagnosis of Major Depressive Disorder (MDD). However, the clinical …
accurate diagnosis of Major Depressive Disorder (MDD). However, the clinical …
Improving the diagnosis of psychiatric disorders with self-supervised graph state space models
Single subject prediction of brain disorders from neuroimaging data has gained increasing
attention in recent years. Yet, for some heterogeneous disorders such as major depression …
attention in recent years. Yet, for some heterogeneous disorders such as major depression …
[HTML][HTML] Joint learning of multi-level dynamic brain networks for autism spectrum disorder diagnosis
N Li, J Xiao, N Mao, D Cheng, X Chen, F Zhao… - Computers in Biology …, 2024 - Elsevier
Graph convolutional networks (GCNs), with their powerful ability to model non-Euclidean
graph data, have shown advantages in learning representations of brain networks …
graph data, have shown advantages in learning representations of brain networks …
Spatiotemporal modeling of multivariate signals with graph neural networks and structured state space models
Multivariate signals are prevalent in various domains, such as healthcare, transportation
systems, and space sciences. Modeling spatiotemporal dependencies in multivariate …
systems, and space sciences. Modeling spatiotemporal dependencies in multivariate …