Regressors are not normally orthogonalised in SPM, because there is rarely any need (see SPM course slides about correlated regressors and orthogonalisation). However, on situation in which serial orthogonalisation is applied is for multiple parametric modulations of a single trial-type. The main rationale for this is that multiple modulators normally arise in the context of polynomial expansions (where a modulator is expanded into linear, quadratic, cubic etc terms), and here one normally wants to orthogonalise the Nth order term with respect to the 1, 2,.. N-1th order terms (so assigning any shared variance to the lower order terms - see course notes). However, another use for multiple parametric modulations is when one wants to covary out a parametric factor (eg RTs) across multiple conditions. This can be done by specifying a single trial-type (ie all onsets), and multiple modulators, one for the covariate of no interest, and others that code the different conditions (which are binary variables). [If one specifies multiple event-types, one per condition, and modulates each of these by the RTs, this does not covary out the RT across all conditions, because each modulator is mean-corrected, and so any difference in mean RTs across conditions is not covaried out.]

The solution to the problem of serial orthogonalisation of parametric modulators when those modulators are linearly dependent is to comment out line 229 on spm_get_ons.m.

You can copy this function from /imaging/local/spm/spm5/ to ~/matlab (assuming that is higher in your path) to make this edit. In the meantime, I will think about changing the local copy that everyone at the CBU uses (and writing a WIKI page).

Note that, if you do comment out this line (i.e, don't orthogonalise the modulators), be careful when you do polynomial expansions of a single modulator variable, because one does typically want linear, quadratic, etc terms to be orthogonalised (so that common variance is assigned to the lower order terms). Note also that if you do comment out this line, and the modulators are linearly-dependent, this will mean that you cannot estimate certain contrasts (namely those that don't sum to zero) - but this doesn't matter if you are always interested in *differences* between modulators, rather than the separate effect of each.

Note also that this problem of linear dependence doesn't normally arise with more typical multiple modulations (eg by some set of continuous variables like RT) that are generally unlikely to be linearly dependent.