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Recent advances in network neuroscience have provided new insights into brain organization in health and disease. In particular, graph theory analyses of brain networks have shown that the human brain is characterized by a high level of integration between distant brain regions and good local communication between neighboring areas. However, these brain networks are normally analyzed using single neuroimaging modalities such as functional magnetic resonance or diffusion tensor imaging. Machine learning techniques for graph structures, such as Graph Neural Networks (GNN), are used to infer and predict from the graph data.
Here we present BRAPH 2.0 (BRain Analysis using graPH theory version 2.0 ), which is a major update of the first version. BRAPH 2..0 Genesis utilizes the capability of an object-oriented programming paradigm and a new engine to provide clear, robust, clean, modular, maintainable, testable, and machine learning ready code.
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