Effective Connectome

Effective connectomics aims to map the causal relationships between brain regions, moving beyond simple correlations to understand directional influences and dynamic interactions underlying brain function. Current research focuses on developing sophisticated models, including Bayesian methods and graph neural networks, to infer these effective connections from multimodal neuroimaging data (EEG, fMRI, DTI), often incorporating directed acyclic graphs (DAGs) and ordinary differential equations (ODEs) to capture temporal dynamics. These advancements improve the accuracy and reliability of brain network mapping, offering insights into brain organization and paving the way for better diagnostic and prognostic tools in neurological and psychiatric disorders. The integration of prior knowledge, such as from diffusion tensor imaging (DTI), further enhances the robustness and interpretability of these models.