fMRI Time Series

fMRI time series analysis focuses on extracting meaningful information from the temporal dynamics of brain activity measured by fMRI, aiming to understand brain function and dysfunction. Current research emphasizes the development and application of advanced machine learning models, including transformers, graph neural networks, and autoencoders, to analyze these complex time series data, often incorporating techniques like topological data analysis and dynamic mode decomposition. These analyses are proving valuable for improving the diagnosis and understanding of neurological and psychiatric disorders like autism, Alzheimer's disease, and mild cognitive impairment, as well as for investigating fundamental aspects of brain network organization and cognitive processes.