Dear SPM community,

Is there any way to apply higher order autoregressive models than AR(1) during classical first level model estimation?

I am attempting to try out the methods described here: http://ieeexplore.ieee.org/document/7552348/​  for better deconvolution of my MVPA conditions in a rapid event-related design. The authors tell me that they implemented their higher order AR models using Bayesian estimation. I have tried this but, unfortunately, for my complex design and high resolution (1.4mm isotropic whole brain) data this seems computationally unfeasible - our high performance cluster has been working on a single subject for a week and is only half way through.

Can anyone point me in the direction of a way to either pre-process my data before estimation or to hack the SPM files to allow higher order AR without the need for Bayesian estimation?

Best wishes,

Thomas