My experiment consists of 5 stages: the first run collects resting-state data, the second run is a pre-task measurement, the third run is a learning phase, the fourth run is a post-task measurement, and the last run collects resting-state data again.
The scanning protocol are consistent across all runs, and the tasks in the second and fourth runs are the same. I want to compare brain activity before and after the learning phase in both resting-state and task conditions. How should I design and fit the general linear model (GLM)? Should I fit a separate GLM for each run? Additionally, the third run's learning phase is long, and I want to separate the early and late parts of this phase to compare differences between early and late learning stages. How should I set up the GLM for those?
How should I design and fit the general linear model (GLM)?Should I fit a separate GLM for each run?
For the two resting-state runs, you may need to analyze them separately if correlations are your focus. However, for the two task-based runs, you can construct a single regression model, provided they are properly aligned.
Additionally, the third run's learning phase is long, and I want to separate the early and late parts of this phase to compare differences between early and late learning stages. How should I set up the GLM for those?
It is difficult to provide suggestions without knowing the specifics of the tasks involved. Generally, you can construct two separate sets of regressors—one for the early stages and one for the late stages—and compare them.
Thank you for your reply!
My confusion is that the tasks of the two task-based runs are the same. If construct a single regression model, both regressors represent the same type of stimulus, but their respective onsets are different. Is this feasible?
If construct a single regression model, both regressors represent the same type of stimulus, but their respective onsets are different. Is this feasible?
You can construct two separate sets of regressors—one for pre-task conditions in the second run and one for post-task conditions in the fourth run—and compare them.
Gang Chen
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