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https://github.com/basiralab/GNNs-in-Network-Neuroscience

A review of papers proposing novel GNN methods with application to brain connectivity published in 2017-2020.
https://github.com/basiralab/GNNs-in-Network-Neuroscience
biomarker brain-connectivity brain-disorders brain-gnn gans geometric-deep-learning gnn gnns graph-fusion graph-integration graph-neural-network graph-prediction graph-superresolution graph-synthesis graph-timeseries network-neuroscience predictive-intelligence
Last synced: 1 day ago
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A review of papers proposing novel GNN methods with application to brain connectivity published in 2017-2020.
Host: GitHub
URL: https://github.com/basiralab/GNNs-in-Network-Neuroscience
Owner: basiralab
Created: 2021-06-07T07:31:38.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2023-02-27T16:25:19.000Z (over 1 year ago)
Last Synced: 2024-08-03T15:10:50.113Z (3 months ago)
Topics: biomarker, brain-connectivity, brain-disorders, brain-gnn, gans, geometric-deep-learning, gnn, gnns, graph-fusion, graph-integration, graph-neural-network, graph-prediction, graph-superresolution, graph-synthesis, graph-timeseries, network-neuroscience, predictive-intelligence
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Stars: 202
Watchers: 4
Forks: 27
Open Issues: 0
Metadata Files:
- Readme: README.md
Awesome Lists containing this project

README

        # Graph Neural Networks in Network Neuroscience

> In Geometric Deep Learning (GDL), one of the most popular learning methods is the Graph Neural Network (GNN), which applies convolutional layers to learn the topological structure of the input graph. GNNs have recently been used for the analysis of different types of the human connectome, such as structural, functional, and morphological networks derived respectively from Diffusion Tensor Imaging (DTI), functional magnetic resonance imaging (fMRI), and T1-w MRI data. Such a nascent field has achieved significant performance improvements over traditional deep neural networks in the early diagnosis of brain disorders. Thus, we list here all MICCAI papers of 4 years (2017-2020), some journals, and IPMI (2017-2019) papers focusing on different learning tasks:

* [Predicting one or multiple modalities from a single or different modalities (e.g., data synthesis)](#SingleMultiviewprediction)

* [Predicting high-resolution data from low-resolution data](#resolution)

* [Predicting the brain evolution trajectory](#timedependent)

* [Population network integration and fusion](#Integrationfusion)

* [Computer-aided prognostic methods (e.g., for brain diseases)](#diseaseclassification)

* [Biomarker identification](#biomarker)

* [Extra works related to machine learning](#ML)

If you like to update the file by adding the unlisted open-source articles, feel free to open an issue or submit a pull requests. You can also directly contact Alaa Bessadok at [email protected]. All contributions are very welcome! 



# arXiv link

The full paper is downloadable at 

https://arxiv.org/pdf/2106.03535.pdf

## Cross-domain graph prediction

### Single graph prediction

| Title                                                        | Paper | Author | Dataset | Code | Youtube Video |  Proceeding/Journal/Year |

|:------------------------------------------------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:| :----------------------:|:----------------------: 

| Recovering Brain Structural Connectivity from Functional Connectivity via Multi-GCN Based Generative Adversarial Network | [LNCS](https://link.springer.com/chapter/10.1007/978-3-030-59728-3_6)  | Lu Zhang | [HCP](https://www.humanconnectome.org/study/hcp-young-adult/data-releases)   | __ | __ | MICCAI 2020 | 

| Topology-Guided Cyclic Brain Connectivity Generation using Geometric Deep Learning| [ScienceDirect](https://www.sciencedirect.com/science/article/pii/S0165027020304118) | Abubakhari Sserwadda | [ABIDE](http://fcon_1000.projects.nitrc.org/indi/abide/)   | [Python](https://github.com/basiralab/CGTS-GAN) | __ | Journal of Neuroscience Methods 2020

| Deep Representation Learning for Multimodal Brain Networks | [ARXIV](https://arxiv.org/abs/2007.09777)  | Wen Zhang | [WU-Minn HCP](https://pubmed.ncbi.nlm.nih.gov/23684880/)   | __ | __ | MICCAI 2020  

| Symmetric Dual Adversarial Connectomic Domain Alignment for Predicting Isomorphic Brain Graph from a Baseline Graph | [LNCS](https://link.springer.com/chapter/10.1007/978-3-030-32251-9_51)  | Alaa Bessadok |  [ABIDE](http://fcon_1000.projects.nitrc.org/indi/abide/)   | __ | __ | MICCAI 2019 

| Hierarchical Adversarial Connectomic Domain Alignment for Target Brain Graph Prediction and Classification from a Source Graph | [LNCS](https://link.springer.com/chapter/10.1007/978-3-030-32281-6_11)  | Alaa Bessadok |  [ABIDE](http://fcon_1000.projects.nitrc.org/indi/abide/)   | [Python](https://github.com/basiralab/HADA) | [14min](https://www.youtube.com/watch?v=OJOtLy9Xd34) | PRIME-MICCAI 2019 

| Brain graph synthesis by dual adversarial domain alignment and target graph prediction from a source graph | [LNCS](https://www.sciencedirect.com/science/article/pii/S1361841520302668?via%3Dihub)  | Alaa Bessadok |  [ABIDE](http://fcon_1000.projects.nitrc.org/indi/abide/)   | __ | __ | Medical Image Analysis Journal 2021 

### Multi-graph prediction

| Title                                                        | Paper | Author | Dataset | Code | Youtube Video | Proceeding/Journal/Year |

|:------------------------------------------------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:| :----------------------:|:----------------------: 

| Topology-Aware Generative Adversarial Network for Joint Prediction of Multiple Brain Graphs from a Single Brain Graph | [ARXIV](https://arxiv.org/abs/2009.11058)  | Alaa Bessadok | [ABIDE](http://fcon_1000.projects.nitrc.org/indi/abide/)   | [Python]( https://github.com/basiralab/MultiGraphGAN) | [10min](https://www.youtube.com/watch?v=vEnzMQqbdHc) | MICCAI 2020



## Cross-resolution graph prediction

| Title                                                        | Paper | Author | Dataset | Code | Youtube Video | Proceeding/Journal/Year |

|:------------------------------------------------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:| :----------------------:|:----------------------: 

| GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain Connectomes | [ARXIV](https://arxiv.org/abs/2009.11080)  | Megi Isallari  |  [Southwest University Longitudinal Imaging Multimodal (SLIM) study](https://www.nature.com/articles/sdata201717)   | [Python](https://github.com/basiralab/GSR-Net) | [11min](https://www.youtube.com/watch?v=xwHKRxgMaEM) | MLMI-MICCAI 2020



## Cross-time graph prediction

| Title                                                        | Paper | Author | Dataset | Code | Youtube Video | Proceeding/Journal/Year |

|:------------------------------------------------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:| :----------------------:|:----------------------: 

| Deep EvoGraphNet Architecture for Time-Dependent Brain Graph Data Synthesis from a Single Timepoint | [ARXIV](https://arxiv.org/abs/2009.13217)  | Ahmed Nebli  |  [OASIS-2](https://www.oasis-brains.org/)   | [Python](https://github.com/basiralab/EvoGraphNet) | [6min](https://www.youtube.com/watch?v=aT---t2OBO0) | PRIME-MICCAI 2020 

| Residual Embedding Similarity-Based Network Selection for Predicting Brain Network Evolution Trajectory from a Single Observation  | [ARXIV](https://arxiv.org/abs/2009.11110)  | Ahmet Serkan Goktas | [ADNI GO](https://pubmed.ncbi.nlm.nih.gov/16443497/) | [Python](https://github.com/basiralab/RESNets) | [6min](https://www.youtube.com/watch?v=UOUHe-1FfeY) | PRIME-MICCAI 2020 

| Foreseeing Brain Graph Evolution over Time Using Deep Adversarial Network Normalizer | [ARXIV](https://arxiv.org/abs/2009.11166)  | Zeynep Gurler  | [OASIS-2](https://www.oasis-brains.org/)   | [Python](https://github.com/basiralab/gGAN) | [6min](https://www.youtube.com/watch?v=5vpQIFzf2Go) | PRIME-MICCAI 2020 



## Integration/Fusion

| Title                                                        | Paper | Author | Dataset | Code | Youtube Video | Proceeding/Journal/Year |

|:------------------------------------------------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:| :----------------------:|:----------------------:  

| Deep Graph Normalizer: A Geometric Deep Learning Approach for Estimating Connectional Brain Templates | [LNCS](https://link.springer.com/chapter/10.1007/978-3-030-59728-3_16)  | Mustafa Burak Gurbuz | [ADNI](http://adni.loni.usc.edu/) [ABIDE](http://fcon_1000.projects.nitrc.org/indi/abide/) | [Python]( https://github.com/basiralab/DGN) | [10min](https://www.youtube.com/watch?v=Q_WLY2ZNxRk) | MICCAI 2020  

| Clustering-Based Deep Brain MultiGraph Integrator Network for Learning Connectional Brain Templates | [LNCS](https://link.springer.com/chapter/10.1007/978-3-030-60365-6_11)  | Uğur Demir | [ABIDE](http://fcon_1000.projects.nitrc.org/indi/abide/)   | [Python]( https://github.com/basiralab/cMGINet) | __ | GRAIL-MICCAI 2020 



## Brain graph classification

### Graph embedding-based methods

| Title                                                        | Paper | Author | Dataset | Code | Youtube Video | Proceeding/Journal/Year |

|:------------------------------------------------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:| :----------------------:|:----------------------:  

| Multi-view Brain HyperConnectome AutoEncoder for Brain State Classification | [LNCS](https://arxiv.org/abs/2009.11553)  | Alin Banka  | [ADNI GO](https://pubmed.ncbi.nlm.nih.gov/16443497/)   | [Python](https://github.com/basiralab/HCAE) | [6min](https://www.youtube.com/watch?v=ncPyj_4cSe8) | PRIME-MICCAI 2020 

| Adversarial Connectome Embedding for Mild Cognitive Impairment Identification Using Cortical Morphological Networks | [LNCS](https://link.springer.com/chapter/10.1007/978-3-030-32391-2_8)  | Alin Banka |  [ADNI GO](https://pubmed.ncbi.nlm.nih.gov/16443497/)   | __ | __ | CIN-MICCAI 2019 

| Deep Hypergraph U-Net for Brain Graph Embedding and Classification | [ARXIV](https://arxiv.org/abs/2008.13118)  | Mert Lostar |  [ADNI GO](https://pubmed.ncbi.nlm.nih.gov/16443497/)   | [Python](https://github.com/basiralab/HUNet) | __ | 2020

| Interpretable Multimodality Embedding of Cerebral Cortex Using Attention Graph Network for Identifying Bipolar Disorder | [LNCS](https://link.springer.com/chapter/10.1007/978-3-030-32248-9_89) | Huzheng Yang | __  | __ | __ | MICCAI 2019

| New Graph-Blind Convolutional Network for Brain Connectome Data Analysis  | [LNCS](https://link.springer.com/chapter/10.1007/978-3-030-20351-1_52)  | Yanfu Zhang | [HCP](https://www.humanconnectome.org)  | __ | __ | IPMI 2019

| Attention-Diffusion-Bilinear Neural Network for Brain Network Analysis | [PubMed](https://pubmed.ncbi.nlm.nih.gov/32070948/)  | Jiashuang Huang | Epilepsy  dataset | __ | __ | IEEE Transactions on Medical Imaging 2020

### Graph-based methods

| Title                                                        | Paper | Author | Dataset | Code | Youtube Video | Proceeding/Journal/Year |

|:------------------------------------------------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:| :----------------------:|:----------------------: 

| Metric learning with spectral graph convolutions on brain connectivity networks  | [ScienceDirect](https://www.sciencedirect.com/science/article/pii/S1053811917310765)  | Sofia Ira Ktena | [ABIDE](http://fcon_1000.projects.nitrc.org/indi/abide/)  [UK Biobank] | [Python](https://github.com/sk1712/gcn_metric_learning) | __ | Neuroimage Journal 2018

| Dynamic Spectral Graph Convolution Networks with Assistant Task Training for Early MCI Diagnosis | [LNCS](https://link.springer.com/chapter/10.1007/978-3-030-32251-9_70) | Xiaodan Xing | [ADNI2](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2544629/)   | __ | __ | MICCAI 2019

| A Cascaded Multi-modality Analysis in Mild Cognitive Impairment | [LNCS](https://link.springer.com/chapter/10.1007/978-3-030-32692-0_64) | Lu Zhang | [ADNI 3](http://adni.loni.usc.edu/)  | __ | __ | MLMI-MICCAI 2019

| DS-GCNs: Connectome Classification Using Dynamic Spectral Graph Convolution Networks with Assistant Task Training  | [ARXIV](https://arxiv.org/pdf/2001.03057.pdf)  | Xiaodan Xing | [ABIDE]( http://adni.loni.usc.edu)  | __ | __ | Cerebral Cortex 2021

| Multi-Hops Functional Connectivity Improves Individual Prediction of Fusiform Face Activation via a Graph Neural Network| [Journal website](https://www.frontiersin.org/articles/10.3389/fnins.2020.596109/full) | Dongya Wu | [HCP](https://www.humanconnectome.org) | __ | __ | Frontiers in Neuroscience 2021

### Population-based methods

| Title                                                        | Paper | Author | Dataset | Code | Youtube Video | Proceeding/Journal/Year |

|:------------------------------------------------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:| :----------------------:|:----------------------: 

| Integrating Similarity Awareness and Adaptive Calibration in Graph Convolution Network to Predict Disease | [LNCS](https://link.springer.com/chapter/10.1007/978-3-030-59728-3_13)  | Xuegang Song  | [ADNI](http://adni.loni.usc.edu/) | __ | __ | MICCAI 2020 

| Spectral Graph Convolutions for Population-Based Disease Prediction   | [ARXIV](https://arxiv.org/abs/1703.03020)  | Sarah Parisot  | [ADNI](http://adni.loni.usc.edu/) [ABIDE](http://fcon_1000.projects.nitrc.org/indi/abide/)  | [Python](https://github.com/parisots/population-gcn) | __ | MICCAI 2017

| InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction  | [ARXIV](https://arxiv.org/abs/1903.04233)  | Anees Kazi | [ABIDE](http://fcon_1000.projects.nitrc.org/indi/abide/) [TADPOLE](http://adni.loni.usc.edu/)  | __ | __ | IPMI 2019



## Biomarker identification

| Title                                                        | Paper | Author | Dataset | Code | Youtube Video | Proceeding/Journal/Year |

|:------------------------------------------------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:| :----------------------:|:----------------------: 

| Graph Neural Network for Interpreting Task-fMRI Biomarkers | [ARXIV](https://arxiv.org/abs/1907.01661)  | Xiaoxiao Li |  [UNC/UMN Baby Connectome Project](https://pubmed.ncbi.nlm.nih.gov/29578031/)   | [Yale Child Study Center] | __ | MICCAI 2019

| Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis | [ARXIV](https://arxiv.org/abs/2007.14589)  | Xiaoxiao Li  | multi-modal epilepsy dataset   | __ | __ | MICCAI 2020

| Interpretable Multimodality Embedding of Cerebral Cortex Using Attention Graph Network for Identifying Bipolar Disorder | [LNCS](https://link.springer.com/chapter/10.1007/978-3-030-32248-9_89) | Huzheng Yang | __  | __ | __ | MICCAI 2019

| BrainGNN: Interpretable Brain Graph NeuralNetwork for fMRI Analysis| [bioRxiv](https://www.biorxiv.org/content/10.1101/2020.05.16.100057v1) | Xiaoxiao Li | [Biopoint](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5510046/) [HCP](https://www.humanconnectome.org) | __ | __ | bioRxiv 2020



## Machine Learning and other types of algorithm for Network Neuroscience

## Single/Multi-view prediction

| Title                                                        | Paper | Author | Dataset | Code | Youtube Video | Proceeding/Journal/Year |

|:------------------------------------------------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:| :----------------------:|:----------------------: 

| Multi-view Brain Network Prediction from a Source View Using Sample Selection via CCA-Based Multi-kernel Connectomic Manifold Learning  | [LNCS](https://link.springer.com/chapter/10.1007/978-3-030-00320-3_12)  | Minghui Zhu | [ABIDE](http://fcon_1000.projects.nitrc.org/indi/abide/)   | __ | __ | PRIME-MICCAI 2018

## Integration/Fusion

| Title                                                        | Paper | Author | Dataset | Code | Youtube Video | Proceeding/Journal/Year |

|:------------------------------------------------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:| :----------------------:|:----------------------: 

| Supervised Multi-topology Network Cross-Diffusion for Population-Driven Brain Network Atlas Estimation | [ARXIV](https://arxiv.org/abs/2009.11054)  | Islem Mhiri | [ABIDE](http://fcon_1000.projects.nitrc.org/indi/abide/)   | [Python]( https://github.com/basiralab/SM-netFusion-PY) [MATLAB]( https://github.com/basiralab/SM-netFusion) | [10min](https://www.youtube.com/watch?v=eWz65SyR-eM) | MICCAI 2020 

| Multi-scale Profiling of Brain Multigraphs by Eigen-Based Cross-diffusion and Heat Tracing for Brain State Profiling | [ARXIV](https://arxiv.org/abs/2009.11534)  | Mustafa Saglam | [ABIDE](http://fcon_1000.projects.nitrc.org/indi/abide/)   | __ |  [8min](https://www.youtube.com/watch?v=D_E2m6O37mk) | GRAIL-MICCAI 2020 

| Estimation of Brain Network Atlases UsingDiffusive-Shrinking Graphs:Application to Developing Brains | [LNCS](https://link.springer.com/chapter/10.1007/978-3-319-59050-9_31) | Islem Rekik |  UNC/UMN Baby Connectome dataset   | __ |  __ | IPMI 2017 

## Resolution

| Title                                                        | Paper | Author | Dataset | Code | Youtube Video | Proceeding/Journal/Year |

|:------------------------------------------------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:| :----------------------:|:----------------------: 

| Predicting High-Resolution Brain Networks Using Hierarchically Embedded and Aligned Multi-resolution Neighborhoods | [LNCS](https://link.springer.com/chapter/10.1007/978-3-030-32281-6_12)  | Kübra Cengiz | [ABIDE](http://fcon_1000.projects.nitrc.org/indi/abide/)  | __ | __ | PRIME-MICCAI 2019

## Time-dependent

| Title                                                        | Paper | Author | Dataset | Code | Youtube Video | Proceeding/Journal/Year |

|:------------------------------------------------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:| :----------------------:|:----------------------: 

| Progressive Infant Brain Connectivity Evolution Prediction from Neonatal MRI Using Bidirectionally Supervised Sample Selection | [LNCS](https://link.springer.com/chapter/10.1007/978-3-030-32281-6_7)  | Olfa Ghribi | __ | __ | __ | MICCAI 2019

| Learning-Guided Infinite Network Atlas Selection for Predicting Longitudinal Brain Network Evolution from a Single Observation | [LNCS](https://link.springer.com/chapter/10.1007/978-3-030-32245-8_88)  | Baha Eddine Ezzine  | [ADNI GO](https://pubmed.ncbi.nlm.nih.gov/16443497/)   | __ | __ | MICCAI 2019

## Disease classification

| Title                                                        | Paper | Author | Dataset | Code | Youtube Video | Proceeding/Journal/Year |

|:------------------------------------------------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:| :----------------------:|:----------------------: 

| Graph Morphology-Based Genetic Algorithm for Classifying Late Dementia States | [LNCS](https://link.springer.com/chapter/10.1007/978-3-030-32391-2_3) | Oumaima Ben Khelifa  | __  | __ | __ | CNI-MICCAI 2019

| Joint Correlational and Discriminative Ensemble Classifier Learning for Dementia Stratification Using Shallow Brain Multiplexes | [LNCS](https://link.springer.com/chapter/10.1007/978-3-030-00928-1_68)  | Rory Raeper  | [ABIDE](http://fcon_1000.projects.nitrc.org/indi/abide/)   | __ | __ | PRIME-MICCAI 2018

## Other predictions

| Title                                                        | Paper | Author | Dataset | Code | Youtube Video | Proceeding/Journal/Year |

|:------------------------------------------------------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:| :----------------------:|:----------------------: 

| Predicting Emotional Intelligence Scores from Multi-session Functional Brain Connectomes | [LNCS](https://link.springer.com/chapter/10.1007/978-3-030-00320-3_13)  | Anna Lisowska | [SLIM](https://www.nature.com/articles/sdata201717) | __ | __ | PRIME-MICCAI 2018

# Please cite the following paper when using our work:

```latex

@article{bessadok2022graph,

  title={Graph neural networks in network neuroscience},

  author={Bessadok, Alaa and Mahjoub, Mohamed Ali and Rekik, Islem},

  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},

  year={2022},

  publisher={IEEE}

}

```