Connectivity Matrix

Connectivity matrices represent the relationships between different brain regions, providing a powerful tool for understanding brain function and dysfunction. Current research focuses on developing sophisticated algorithms, including graph neural networks and diffusion models, to analyze these matrices, particularly for dynamic functional connectivity and incorporating structural information. These advancements enable more accurate diagnoses of neurological and cognitive disorders, such as depression and Alzheimer's disease, by identifying characteristic connectivity patterns and improving the interpretability of diagnostic models. Ultimately, this work aims to refine our understanding of brain organization and its relationship to cognitive abilities and disease.