Functional Network

Functional networks represent the interconnectedness of brain regions, revealing how different areas collaborate during cognitive processes or in disease states. Current research focuses on leveraging advanced machine learning techniques, including graph neural networks (GNNs) like graph convolutional networks and transformers, to analyze these networks, identify biomarkers for neurological disorders (e.g., ADHD, Parkinson's Disease, mood disorders), and improve the interpretability of deep learning models themselves. These analyses are revealing sex differences in brain lateralization, identifying multi-scale features predictive of treatment response, and uncovering shared network disruptions across various brain disorders. This work has significant implications for diagnostics, personalized medicine, and a deeper understanding of brain function.