Dual regression is maybe the simplest way to obtain subject-specific estimates of ICA-based resting-state networks (RSNs). RSNs are regions of the brain that tend to act in a coordinated manner in the absence of a specific task, such as the three shown below (from a 50-component ICA of the Human Connectome Project).

Popular ICA toolboxes for fMRI analysis like GIFT and MELODIC can perform dual regression, but such a simple approach is also straightforward to implement one one’s own. I end up doing this a lot (and would love to hear in the comments whether others do too or prefer to use existing implementations). This post is about the important role of centering in dual regression, which is easy to overlook (at least for me) when implementing this simple technique.

**… this post was originally written on my old blog. Read the full post here. **