Inferring Brain Dynamics via Multimodal Joint Graph Representation EEG-fMRI
Recent studies have shown that multi-modeling methods can provide new insights into the analysis of brain components that are not possible when each modality is acquired separately. The joint representations of different modalities is a robust model to analyze simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI). Advances in precision instruments have given us the ability to observe the spatiotemporal neural dynamics of the human brain through non-invasive neuroimaging techniques such as EEG & fMRI. Nonlinear fusion methods of streams can extract effective brain components in different dimensions of temporal and spatial. Graph-based analyzes, which have many similarities to brain structure, can overcome the complexities of brain mapping analysis. Throughout, we outline the correlations of several different media in time shifts from one source with graph-based and deep learning methods. Determining overlaps can provide a new perspective for diagnosing functional changes in neuroplasticity studies.
Code (0)
등록된 구현이 없습니다.
Tasks
EEGElectroencephalogram (EEG)Similar Papers 제목 키워드 기반
BrainDyn: A Sheaf Neural ODE for Generative Brain Dynamics
Efficient neural network models that generate brain-like dynamic activity can be a valuable resource for generating synthetic data, analyzing differences in brain transients under conditions such as testing perturbation …
JEDI: Jointly Embedded Inference of Neural Dynamics
Animal brains flexibly and efficiently achieve many behavioral tasks with a single neural network. A core goal in modern neuroscience is to map the mechanisms of the brain's flexibility onto the dynamics underlying neura…
Brain Multigraph Prediction using Topology-Aware Adversarial Graph Neural Network
Brain graphs (i.e, connectomes) constructed from medical scans such as magnetic resonance imaging (MRI) have become increasingly important tools to characterize the abnormal changes in the human brain. Due to the high ac…
Generative Adversarial NetworkGraph GenerationGraph Neural NetworkJoint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis
Multimodal brain networks characterize complex connectivities among different brain regions from both structural and functional aspects and provide a new means for mental disease analysis. Recently, Graph Neural Networks…
Contrastive LearningMultimodal Graph Neural Networks for Prognostic Modeling of Brain Network Reorganization
Understanding the dynamic reorganization of brain networks is critical for predicting cognitive decline, neurological progression, and individual variability in clinical outcomes. This work proposes a multimodal graph ne…
Graph Neural Network