The field of neuroimaging and machine learning is rapidly evolving, with a focus on improving disease diagnosis and treatment. Recent studies have explored the use of deep learning techniques, such as weighted voting ensemble models and generative diffusion models, to enhance the accuracy of stroke diagnosis and crystal grain analysis. Additionally, novel approaches to brain network analysis, such as edge-boosted graph learning, have shown promising results in predicting disease states from functional brain connectivity. Diffusion bridge models have also been proposed for 3D medical image translation, enabling cross-modality data augmentation and reducing the need for extensive DTI acquisition. Noteworthy papers include the proposal of a concept-oriented synthetic data approach for training generative AI-driven crystal grain analysis, which achieved an average accuracy of 97.23%. The edge-boosted graph learning approach for functional brain connectivity analysis also demonstrated significant improvements over state-of-the-art GNN methods. Furthermore, a few-shot metric learning method with dual-channel attention was proposed for cross-modal same-neuron identification, enabling robust and accurate matching across different imaging modalities.