Graph Neural Networks for Brain Graph Learning: A Survey
- URL: http://arxiv.org/abs/2406.02594v1
- Date: Sat, 1 Jun 2024 02:47:39 GMT
- Title: Graph Neural Networks for Brain Graph Learning: A Survey
- Authors: Xuexiong Luo, Jia Wu, Jian Yang, Shan Xue, Amin Beheshti, Quan Z. Sheng, David McAlpine, Paul Sowman, Alexis Giral, Philip S. Yu,
- Abstract summary: Graph neural networks (GNNs) have demonstrated a significant advantage in mining graph-structured data.
GNNs to learn brain graph representations for brain disorder analysis has recently gained increasing attention.
In this paper, we aim to bridge this gap by reviewing brain graph learning works that utilize GNNs.
- Score: 53.74244221027981
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Exploring the complex structure of the human brain is crucial for understanding its functionality and diagnosing brain disorders. Thanks to advancements in neuroimaging technology, a novel approach has emerged that involves modeling the human brain as a graph-structured pattern, with different brain regions represented as nodes and the functional relationships among these regions as edges. Moreover, graph neural networks (GNNs) have demonstrated a significant advantage in mining graph-structured data. Developing GNNs to learn brain graph representations for brain disorder analysis has recently gained increasing attention. However, there is a lack of systematic survey work summarizing current research methods in this domain. In this paper, we aim to bridge this gap by reviewing brain graph learning works that utilize GNNs. We first introduce the process of brain graph modeling based on common neuroimaging data. Subsequently, we systematically categorize current works based on the type of brain graph generated and the targeted research problems. To make this research accessible to a broader range of interested researchers, we provide an overview of representative methods and commonly used datasets, along with their implementation sources. Finally, we present our insights on future research directions. The repository of this survey is available at \url{https://github.com/XuexiongLuoMQ/Awesome-Brain-Graph-Learning-with-GNNs}.
Related papers
- DBGDGM: Dynamic Brain Graph Deep Generative Model [63.23390833353625]
Graphs are a natural representation of brain activity derived from functional magnetic imaging (fMRI) data.
It is well known that clusters of anatomical brain regions, known as functional connectivity networks (FCNs), encode temporal relationships which can serve as useful biomarkers for understanding brain function and dysfunction.
Previous works, however, ignore the temporal dynamics of the brain and focus on static graphs.
We propose a dynamic brain graph deep generative model (DBGDGM) which simultaneously clusters brain regions into temporally evolving communities and learns dynamic unsupervised node embeddings.
arXiv Detail & Related papers (2023-01-26T20:45:30Z) - Benchmarking Graph Neural Networks for FMRI analysis [0.0]
Graph Neural Networks (GNNs) have emerged as a powerful tool to learn from graph-structured data.
We study and evaluate the performance of five popular GNN architectures in diagnosing major depression disorder and autism spectrum disorder.
We highlight that creating optimal graph structures for functional brain data is a major bottleneck hindering the performance of GNNs.
arXiv Detail & Related papers (2022-11-16T14:16:54Z) - Data-Driven Network Neuroscience: On Data Collection and Benchmark [6.796086914275059]
This paper presents a collection of functional human brain network data for potential research in the intersection of neuroscience, machine learning, and graph analytics.
The datasets originate from 6 different sources, cover 4 brain conditions, and consist of a total of 2,702 subjects.
arXiv Detail & Related papers (2022-11-11T02:14:28Z) - DynDepNet: Learning Time-Varying Dependency Structures from fMRI Data
via Dynamic Graph Structure Learning [58.94034282469377]
We propose DynDepNet, a novel method for learning the optimal time-varying dependency structure of fMRI data induced by downstream prediction tasks.
Experiments on real-world fMRI datasets, for the task of sex classification, demonstrate that DynDepNet achieves state-of-the-art results.
arXiv Detail & Related papers (2022-09-27T16:32:11Z) - Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling
Model [64.29487107585665]
Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases.
Here, we propose an interpretable hierarchical signed graph representation learning model to extract graph-level representations from brain functional networks.
In order to further improve the model performance, we also propose a new strategy to augment functional brain network data for contrastive learning.
arXiv Detail & Related papers (2022-07-14T20:03:52Z) - Functional2Structural: Cross-Modality Brain Networks Representation
Learning [55.24969686433101]
Graph mining on brain networks may facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases.
We propose a novel graph learning framework, known as Deep Signed Brain Networks (DSBN), with a signed graph encoder.
We validate our framework on clinical phenotype and neurodegenerative disease prediction tasks using two independent, publicly available datasets.
arXiv Detail & Related papers (2022-05-06T03:45:36Z) - Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning
and Neuroscience (VesselGraph) [3.846749674808336]
We present an extendable dataset of whole-brain vessel graphs based on specific imaging protocols.
We benchmark numerous state-of-the-art graph learning algorithms on the biologically relevant tasks of vessel prediction and vessel classification.
Our work paves a path towards advancing graph learning research into the field of neuroscience.
arXiv Detail & Related papers (2021-08-30T13:40:48Z) - Graph Neural Networks in Network Neuroscience [1.6114012813668934]
graph neural network (GNN) provides a clever way of learning the deep graph structure.
GNN-based methods have been used in several applications related to brain graphs such as missing brain graph synthesis and disease classification.
We conclude by charting a path toward a better application of GNN models in network neuroscience field for neurological disorder diagnosis and population graph integration.
arXiv Detail & Related papers (2021-06-07T11:49:57Z) - Understanding Graph Isomorphism Network for rs-fMRI Functional
Connectivity Analysis [49.05541693243502]
We develop a framework for analyzing fMRI data using the Graph Isomorphism Network (GIN)
One of the important contributions of this paper is the observation that the GIN is a dual representation of convolutional neural network (CNN) in the graph space.
We exploit CNN-based saliency map techniques for the GNN, which we tailor to the proposed GIN with one-hot encoding.
arXiv Detail & Related papers (2020-01-10T23:40:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.