Regarding the white and grey matter nuisance regressors that you asked about on June 29: Those are signals extracted from segmentations (or masks) of those respective tissue types. You can get those masks with SPM's segmentation tool and then you can extract signal from those masks and use those as well as their derivatives as regressors of no-interest. You might want to look in the literature what experts in the field do for task-based paradigms like yours because regression of gray matter only signal is not common and --for task-based fMRI-- neither is regression of white matter signal.
Quick note regarding your June 30 e-mail: you are probably interested in *regressing out* the head motion parameters and not in looking at "the contrast associated with motion" (although you can do that...). It is always good to plot the regressors --at least in a few subjects-- that you are going to regress out to learn more about them.
For novice, I highly recommend "Handbook of Functional MRI Analysis" by Poldrack, Mumford and Nichols:
and to check out Jeanette Mumford (mumfordbrainstats) and Andrew Jahn on youtube.
Specific for resting state fMRI using seed-based methods people have looked a lot at noise removal using nuisance regression. One paper on many regressors that one can add for resting state fMRI is Satterthwaite et al. 20013: http://www.ncbi.nlm.nih.gov/pubmed/22926292. Not all of that is relevant for task-based fMRI though.