I want to perfrom a seed-based functional connectivity analysis. Before that, I want to regress out noise from the voxel time series. I found a clear justification for treating the 6 head motion parameters, CSF and grey matter signals as nuisance regressors. However, based on e.g. Murphy et al. (2009) I am not quite sure whether to put the global mean signal as an additional regressor or not. Given that I want to compare my results to previous studies (which put the Global mean signal as an regressor) I feel almost "forced" to put it in too, despite of the fact that I am convinced that it might induces artificial anticorrelations.
Any opinions regarding that issue? What would you suggest?