fMRI Analysis

fMRI analysis aims to extract meaningful information from brain activity patterns measured by functional magnetic resonance imaging, primarily for understanding cognitive processes and diagnosing neurological disorders. Current research heavily utilizes deep learning, employing architectures like graph neural networks (GNNs), convolutional neural networks (CNNs), and state-space models to analyze both spatial and temporal aspects of fMRI data, often incorporating techniques like data augmentation and domain adaptation to address data limitations and heterogeneity. These advancements are improving the accuracy and interpretability of fMRI-based diagnoses and furthering our understanding of brain function, with implications for personalized medicine and neuroscience research.