Adaptive critical subgraph mining for cognitive impairment conversion prediction with T1-MRI-based brain network
Prediction conversion of early-stage dementia is challenging due to subtle cognitive and
structural brain changes. Traditional T1-weighted magnetic resonance imaging (T1-MRI) …
structural brain changes. Traditional T1-weighted magnetic resonance imaging (T1-MRI) …
Multi-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusion
In the realm of neuroscience, identifying distinctive patterns associated with neurological
disorders via brain networks is crucial. Resting-state functional magnetic resonance imaging …
disorders via brain networks is crucial. Resting-state functional magnetic resonance imaging …
RS-MAE: Region-State Masked Autoencoder for Neuropsychiatric Disorder Classifications Based on Resting-State fMRI
H Ma, Y Xu, L Tian - IEEE Transactions on Neural Networks and …, 2024 - ieeexplore.ieee.org
Dynamic functional connectivity (DFC) extracted from resting-state functional magnetic
resonance imaging (fMRI) has been widely used for neuropsychiatric disorder …
resonance imaging (fMRI) has been widely used for neuropsychiatric disorder …
An objective quantitative diagnosis of depression using a local-to-global multimodal fusion graph neural network
S Liu, J Zhou, X Zhu, Y Zhang, X Zhou, S Zhang… - Patterns, 2024 - cell.com
This study developed an artificial intelligence (AI) system using a local-global multimodal
fusion graph neural network (LGMF-GNN) to address the challenge of diagnosing major …
fusion graph neural network (LGMF-GNN) to address the challenge of diagnosing major …
BGCSL: An unsupervised framework reveals the underlying structure of large-scale whole-brain functional connectivity networks
H Zhang, W Zeng, Y Li, J Deng, B Wei - Computer Methods and Programs …, 2025 - Elsevier
Abstract Background and Objective: Inferring large-scale brain networks from functional
magnetic resonance imaging (fMRI) provides more detailed and richer connectivity …
magnetic resonance imaging (fMRI) provides more detailed and richer connectivity …
Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification
Understanding neurological disorder is a fundamental problem in neuroscience, which often
requires the analysis of brain networks derived from functional magnetic resonance imaging …
requires the analysis of brain networks derived from functional magnetic resonance imaging …
Long-range Brain Graph Transformer
Understanding communication and information processing among brain regions of interest
(ROIs) is highly dependent on long-range connectivity, which plays a crucial role in …
(ROIs) is highly dependent on long-range connectivity, which plays a crucial role in …
A Class-Aware Representation Refinement Framework for Graph Classification
Abstract Graph Neural Networks (GNNs) are widely used for graph representation learning.
Despite its prevalence, GNN suffers from two drawbacks in the graph classification task, the …
Despite its prevalence, GNN suffers from two drawbacks in the graph classification task, the …
BrainOOD: Out-of-distribution Generalizable Brain Network Analysis
In neuroscience, identifying distinct patterns linked to neurological disorders, such as
Alzheimer's and Autism, is critical for early diagnosis and effective intervention. Graph …
Alzheimer's and Autism, is critical for early diagnosis and effective intervention. Graph …
Patch Target Guided Dual-Branch Deep Multiple Instance Learning for 3D MRI Analysis
M Dai, X Shi, X Zhu, T Pan, K Li - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
Deep multiple instance learning (MIL) has attracted considerable attention in medical image
analysis, since it only requires image-level labels for model training without using fine …
analysis, since it only requires image-level labels for model training without using fine …