State fMRI

State fMRI research focuses on analyzing brain activity patterns from functional magnetic resonance imaging (fMRI) data, primarily resting-state fMRI, to understand brain function and dysfunction. Current research employs advanced machine learning techniques, including transformer models and deep convolutional neural networks, often incorporating self-supervised learning and multi-scale analysis to improve accuracy and interpretability in identifying brain network characteristics and classifying neurological conditions. These methods are applied to diverse applications, such as autism detection and understanding neurodegeneration in mild cognitive impairment, offering valuable insights into brain organization and disease processes. The development of robust multimodal registration pipelines further enhances the integration of fMRI data with other neuroimaging modalities for a more comprehensive understanding of the brain.