ArticleIEEE transactions on bio-medical engineering2022
Brain Functional Connectivity Analysis via Graphical Deep Learning.
Article in IEEE transactions on bio-medical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- An interpretable cross-attentive multi-modal MRI fusion framework for schizophrenia identification.Neuroimage. Reports · 2026Article
- A multi-view graph neural network framework for Parkinson's disease identification based on dynamic functional connectivity.Frontiers in aging neuroscience · 2026Article
- BrainGeneBot: a framework for variant prioritization and generative pretrained transformer-informed interpretation across polygenic risk score studies.Briefings in bioinformatics · 2025Article
- Integrated brain connectivity analysis with fMRI, DTI, and sMRI powered by interpretable graph neural networks.Medical image analysis · 2025Article
- Article
- MJNMF-GAT: Multi-Task Joint Non-Negative Matrix Factorization Graph Attention Network for Understanding Adolescent Neurodevelopment.IEEE access : practical innovations, open solutions · 2025Article
- Explainable spatio-temporal graph evolution learning with applications to dynamic brain network analysis during development.NeuroImage · 2024Article
- Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks.IEEE transactions on medical imaging · 2024Article
- Combination of static and dynamic neural imaging features to distinguish sensorineural hearing loss: a machine learning study.Frontiers in neuroscience · 2024Article
- Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
8 authors.
Funding
Abstract
objectiveGraphical deep learning models provide a desirable way for brain functional connectivity analysis. However, the application of current graph deep learning models to brain network analysis is challenging due to the limited sample size and complex relationships between different brain regions.
methodIn this work, a graph convolutional network (GCN) based framework is proposed by exploiting the information from both region-to-region connectivities of the brain and subject-subject relationships. We first construct an affinity subject-subject graph followed by GCN analysis. A Laplacian regularization term is introduced in our model to tackle the overfitting problem. We apply and validate the proposed model to the Philadelphia Neurodevelopmental Cohort for the brain cognition study.
resultsExperimental analysis shows that our proposed framework outperforms other competing models in classifying groups with low and high Wide Range Achievement Test (WRAT) scores. Moreover, to examine each brain region's contribution to cognitive function, we use the occlusion sensitivity analysis method to identify cognition-related brain functional networks. The results are consistent with previous research yet yield new findings. CONCLUSION AND SIGNIFICANCE: Our study demonstrates that GCN incorporating prior knowledge about brain networks offers a powerful way to detect important brain networks and regions associated with cognitive functions.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.