Integrating multimodal and longitudinal neuroimaging data with multi-source network representation learning
Uncovering the complex network of the brain is of great interest to the field of neuroimaging.
Mining from these rich datasets, scientists try to unveil the fundamental biological …
Mining from these rich datasets, scientists try to unveil the fundamental biological …
Multimodal fusion of brain networks with longitudinal couplings
In recent years, brain network analysis has attracted considerable interests in the field of
neuroimaging analysis. It plays a vital role in understanding biologically fundamental …
neuroimaging analysis. It plays a vital role in understanding biologically fundamental …
Estimating functional brain networks by incorporating a modularity prior
Functional brain network analysis has become one principled way of revealing informative
organization architectures in healthy brains, and providing sensitive biomarkers for …
organization architectures in healthy brains, and providing sensitive biomarkers for …
Deep representation learning for multimodal brain networks
Applying network science approaches to investigate the functions and anatomy of the
human brain is prevalent in modern medical imaging analysis. Due to the complex network …
human brain is prevalent in modern medical imaging analysis. Due to the complex network …
MSTGC: multi-channel spatio-temporal graph convolution network for multi-modal brain networks fusion
Multi-modal brain networks characterize the complex connectivities among different brain
regions from structure and function aspects, which have been widely used in the analysis of …
regions from structure and function aspects, which have been widely used in the analysis of …
BrainNetDiff: Generative AI Empowers Brain Network Generation via Multimodal Diffusion Model
Y Zong, S Wang - arXiv preprint arXiv:2311.05199, 2023 - arxiv.org
Brain network analysis has emerged as pivotal method for gaining a deeper understanding
of brain functions and disease mechanisms. Despite the existence of various network …
of brain functions and disease mechanisms. Despite the existence of various network …
Mapping multi-modal brain connectome for brain disorder diagnosis via cross-modal mutual learning
Recently, the study of multi-modal brain connectome has recorded a tremendous increase
and facilitated the diagnosis of brain disorders. In this paradigm, functional and structural …
and facilitated the diagnosis of brain disorders. In this paradigm, functional and structural …
Network-level enrichment provides a framework for biological interpretation of machine learning results
Abstract Machine learning algorithms are increasingly being utilized to identify brain
connectivity biomarkers linked to behavioral and clinical outcomes. However, research often …
connectivity biomarkers linked to behavioral and clinical outcomes. However, research often …
Multimodal graph coarsening for interpretable, MRI-based brain graph neural network
Graph neural networks (GNN s) are a powerful class of model for representation learning on
relational data and graph-structured signal, such as brain connectivity graphs derived from …
relational data and graph-structured signal, such as brain connectivity graphs derived from …
BrainNetDiff: Generative AI Empowers Brain Network Construction Via Multimodal Diffusion
Y Zong, C Jing, JH Chan… - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
Brain network analysis has emerged as a pivotal technology for gaining a deeper
understanding of brain functions and disease mechanisms. However, existing network …
understanding of brain functions and disease mechanisms. However, existing network …