Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

HyperCOCO: Multi-sensory HyperCOgnitive COmputing for learning population level brain connectivity.

Medical image analysis·2026
Same author

Reservoir-Based Graph Convolutional Networks.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Metadata-Driven Federated Learning of Connectional Brain Templates in Non-IID Multi-Domain Scenarios.

IEEE transactions on medical imaging·2025
Same author

Predicting infant brain connectivity with federated multi-trajectory GNNs using scarce data.

Medical image analysis·2025
Same author

FALCON: Feature-Label Constrained Graph Net Collapse for Memory-Efficient GNNs.

IEEE transactions on neural networks and learning systems·2025
Same author

Replica tree-based federated learning using limited data.

Neural networks : the official journal of the International Neural Network Society·2025
Same journal

Wavelet-inspired diffusion model with near-field constraint for real-time echocardiography dehazing.

Medical image analysis·2026
Same journal

Co-assistant networks by pathology foundation model and convolutional neural network for gigapixel whole slide image analysis.

Medical image analysis·2026
Same journal

MBAS2024: A large-scale benchmark for multi-class bi-atrial segmentation in multi-center contrast-enhanced MRIs.

Medical image analysis·2026
Same journal

Respiratory motion augmentation for personalized super-resolution (RMApSR) of 3D cine MR images in MRI-guided radiotherapy.

Medical image analysis·2026
Same journal

Biom3d, a modular framework to host and develop 3D segmentation methods.

Medical image analysis·2026
Same journal

Embracing intra-class heterogeneity for semi-supervised medical image segmentation: From diversity to precision.

Medical image analysis·2026
See all related articles

Related Experiment Video

Updated: Nov 6, 2025

Brain Mapping Using a Graphene Electrode Array
10:32

Brain Mapping Using a Graphene Electrode Array

Published on: October 20, 2023

2.1K

Brain graph super-resolution using adversarial graph neural network with application to functional brain

Megi Isallari1, Islem Rekik2

  • 1BASIRA lab, Faculty of Computer and Informatics Engineering, Istanbul Technical University, Istanbul, Turkey. Electronic address: http://basira-lab.com/.

Medical Image Analysis
|May 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces the first deep graph super-resolution (GSR) framework to automatically generate high-resolution brain graphs from low-resolution data. The novel approach enhances brain image analysis by improving the resolution of complex brain graph structures.

Keywords:
Adversarial learningBrain connectivityGraph neural networkGraph node embeddingGraph super-resolutionSpectral upsampling

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.3K
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

8.1K

Related Experiment Videos

Last Updated: Nov 6, 2025

Brain Mapping Using a Graphene Electrode Array
10:32

Brain Mapping Using a Graphene Electrode Array

Published on: October 20, 2023

2.1K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.3K
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
08:43

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

8.1K

Area of Science:

  • Neuroimaging and Computational Neuroscience
  • Graph Signal Processing
  • Machine Learning for Medical Data

Background:

  • Neuroimaging datasets have increased, driving advancements in brain image analysis.
  • Brain image super-resolution is well-developed, but brain graph super-resolution remains underexplored due to data complexity.
  • Existing methods struggle with the non-Euclidean nature of brain graph data.

Purpose of the Study:

  • To propose the first deep graph super-resolution (GSR) framework for generating high-resolution (HR) brain graphs from low-resolution (LR) counterparts.
  • To address the challenge of increasing the number of nodes (anatomical regions of interest) from N to N' in brain graphs.
  • To develop a method that learns node embeddings based on graph topology rather than solely node attributes.

Main Methods:

  • Formalized GSR as a node feature embedding learning task using a novel, graph-focused Graph U-Net architecture.
  • Incorporated graph spectral theory principles, including a GSR layer and graph convolutional network layers, to enhance node embeddings in the HR graph.
  • Employed adversarial regularization to mitigate domain shift between predicted and ground-truth HR brain graphs.

Main Results:

  • The proposed AGSR-Net framework demonstrated superior performance in predicting high-resolution functional brain graphs from low-resolution inputs compared to its variants.
  • The graph-focused architecture effectively utilizes graph topology for node feature embedding.
  • Adversarial regularization successfully aligned the distributions of predicted and ground-truth HR brain graphs.

Conclusions:

  • The AGSR-Net framework represents a significant advancement in brain graph super-resolution.
  • This novel approach enables the automatic generation of detailed, high-resolution brain graphs, advancing neuroimaging analysis.
  • The framework's code is publicly available, facilitating further research in the field.