MADE-GCN The MADE-GCN (Modality-fusion Adaptive-Dynamic-Edge GCN) is a graph neural network-based tool for detecting depression, which can assist experts in rapidly identifying patients with depression.

The MADE-GCN is a tool that utilizes graph neural networks (GNNs) to detect depression. The software utilizes GNNs to represent the intricate structure of brain networks, and employs graph convolutional processes to depict the influence between brain regions. This approach exhibits enhanced explicability, rendering it more readily embraced by experts. The user inputs the fMRI and sMRI images corresponding to the patient, as well as the utilized brain atlas, into the software, enabling predictions to be made for the patient. In addition to diagnosing patients, the tool can also learn about the relationships between patients and visualize them through composition. Furthermore, diverse cerebral cartography would yield disparate outcomes. Henceforth, the software possesses the capability to assist experts in their diagnostics process through a multi-tiered approach.