ArticleMedical image analysis2022
Predicting brain structural network using functional connectivity.
Article in Medical image analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Towards a general-purpose foundation model for functional MRI analysis.Nature biomedical engineering · 2026Article
- Structuro-Functional Differentiation and Coupling of Gyri and Sulci in the Neonatal Cortex.Human brain mapping · 2026Article
- Conditioned Graph Reconstruction of Brain Functional Network Connectivity Reveals Interpretable Latent Axes of Sex and Fluid Intelligence.bioRxiv : the preprint server for biology · 2026Article
- Generative Adversarial Networks Based on Fine-Grained Image Recognition for the Progression Prediction of Progressive Mild Cognitive Impairment.Interdisciplinary sciences, computational life sciences · 2026Article
- The structural grammar of integration and competition in the human connectome.Frontiers in computational neuroscience · 2026Article
- Oblique Genomics Mixture of Experts: Prediction of Brain Disorder With Aging-Related Changes of Brain's Structural Connectivity Under Genomic Influences.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- A Unified Continuous Staging Framework for Alzheimer's Disease and Lewy Body Dementia via Hierarchical Anatomical Features.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Multidimensional structural-functional coupling uncovers network dysregulation and predicts binge-eating severity in bulimia nervosa.BMC medicine · 2025Article
- Assessment of resting state structural-functional relationships in perisylvian region during the early weeks after birth.Brain structure & function · 2025Article
- Leveraging AI-Driven Neuroimaging Biomarkers for Early Detection and Social Function Prediction in Autism Spectrum Disorders: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Krakencoder: a unified brain connectome translation and fusion tool.Nature methods · 2025Article
- INDIVIDUALIZED TRAJECTORY PREDICTION OF EARLY DEVELOPING FUNCTIONAL CONNECTIVITY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- Early Development and Co-Evolution of Microstructural and Functional Brain Connectomes: A Multi-Modal MRI Study in Preterm and Full-Term Infants.Human brain mapping · 2025Article
- Article
- CSEPC: a deep learning framework for classifying small-sample multimodal medical image data in Alzheimer's disease.BMC geriatrics · 2025Article
- Learning lifespan brain anatomical correspondence via cortical developmental continuity transfer.Medical image analysis · 2025Article
- Brain Network Classification for Accurate Detection of Alzheimer's Disease via Manifold Harmonic Discriminant Analysis.IEEE transactions on neural networks and learning systems · 2024Article
- Comparing structure-function relationships in brain networks using EEG and fNIRS.Scientific reports · 2024Article
- Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model.IEEE transactions on neural networks and learning systems · 2024Article
- ENHANCING GROUP-WISE CONSISTENCY IN 3-HINGE GYRUS MATCHING VIA ANATOMICAL EMBEDDING AND STRUCTURAL CONNECTIVITY OPTIMIZATION.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
Uncovering the non-trivial brain structure-function relationship is fundamentally important for revealing organizational principles of human brain. However, it is challenging to infer a reliable relationship between individual brain structure and function, e.g., the relations between individual brain structural connectivity (SC) and functional connectivity (FC). Brain structure-function displays a distributed and heterogeneous pattern, that is, many functional relationships arise from non-overlapping sets of anatomical connections. This complex relation can be interwoven with widely existed individual structural and functional variations. Motivated by the advances of generative adversarial network (GAN) and graph convolutional network (GCN) in the deep learning field, in this work, we proposed a multi-GCN based GAN (MGCN-GAN) to infer individual SC based on corresponding FC by automatically learning the complex associations between individual brain structural and functional networks. The generator of MGCN-GAN is composed of multiple multi-layer GCNs which are designed to model complex indirect connections in brain network. The discriminator of MGCN-GAN is a single multi-layer GCN which aims to distinguish the predicted SC from real SC. To overcome the inherent unstable behavior of GAN, we designed a new structure-preserving (SP) loss function to guide the generator to learn the intrinsic SC patterns more effectively. Using Human Connectome Project (HCP) dataset and Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset as test beds, our MGCN-GAN model can generate reliable individual SC from FC. This result implies that there may exist a common regulation between specific brain structural and functional architectures across different individuals.
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Registered trials
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.