Hybrid GCN-LSTM Framework for Enhanced Schizophrenia Diagnosis: Integrating Spatial and Temporal Insights from Brain Connectivity Data

A new hybrid approach for diagnosing schizophrenia combines graph convolutional networks (GCNs) with long short-term memory (LSTM) networks, leveraging GCNs for spatial connectivity and LSTMs for temporal modeling. This integration allows a simultaneous examination of spatial and sequential brain connectivity patterns, offering a comprehensive view of schizophrenia's complex dynamics. By extracting critical features from brain connectivity data, the hybrid GCN-LSTM model enhances research insights and improves clinical interpretability. Testing and validation with real-world data demonstrate the model's superior performance over existing methods, advancing clinical decision support in psychiatry. Additionally, this approach not only improves our understanding of schizophrenia's mechanisms but also shows potential for broader applications, such as early and accurate dementia diagnosis. The study marks significant progress in applying advanced machine learning to clinical research.