Using an Ordinary Differential Equation Model to Separate Rest and Task Signals in fMRI

Learn how to apply ordinary differential equation models to effectively separate resting-state and task-related signals in functional magnetic resonance imaging (fMRI) data through this 13-minute conference talk. Discover mathematical modeling techniques that can help distinguish between baseline brain activity and task-specific neural responses, providing insights into brain function analysis and neuroimaging data processing. Explore the theoretical framework and practical applications of ODE-based approaches for signal separation in neuroscience research, with particular focus on how these methods can improve the interpretation of fMRI studies by isolating different components of brain activity.