The field of brain imaging analysis is rapidly advancing with the integration of deep learning techniques and hardware accelerations. Recent developments have focused on improving the efficiency and accuracy of neural network training and deployment, enabling real-time analysis and diagnosis. For instance, the use of field-programmable gate arrays (FPGAs) has been shown to significantly accelerate neural network training, making it possible to perform complex tasks such as brain parameter reconstruction and object detection in a matter of seconds. Additionally, novel deep learning architectures have been proposed to model complex relationships in functional magnetic resonance imaging (fMRI) data, achieving state-of-the-art performance in diagnostic tasks. Notably, simpler convolutional neural networks have been found to outperform more complex attention-based architectures in certain tasks, highlighting the importance of careful model selection and evaluation. Some noteworthy papers in this area include: BrainMT, which introduces a hybrid framework for modeling long-range dependencies in fMRI data, achieving state-of-the-art performance on classification and regression tasks. MvHo-IB, which proposes a multi-view learning framework that integrates higher-order interactions and pairwise interactions for diagnostic decision-making, demonstrating significant improvements over previous methods.