Hi SPM users of resting state fMRI researchers,

I have a question regarding the use of autoregressive model over low-pass fileter in resting state fMRI analyses.
I analyzed the data of rsfMRI by investigating the voxels in the whole-brain, which signal correlate with those of ROIs.
In the analysis, I have used the auto-regressive model instead of low-pass filter. Serial correlations in the BOLD signal were accounted for by a first-degree autoregressive correction (Della-Maggiore et al. 2002) (instead of being removed by a low-pass filter).

But it seems multiple reviewers had problems in use of autoregressive model, intead of low pass filter in rsfMRI analyses. They say low-pass filter has been widely used and recommend low-pass filter.
The use of autoregressive model over the use of low-pass filter has been recommended when the signals of the model frequently changes through time series such as in rapid presentation event related designs (Della-Maggiore et al. 2002). But it seems the reviewers are not pursuaded by this reference and it is true this 2002's paper did not tell anything regarding the use of  autoregressive model in resting state fMRI analyses, it just talks about the cases where the signals of the model frequently changes through time series. 
From what I understand, the low-pass filter has no superiority over autoregressive model in normal fMRI analyses and hence this option is explicitly removed from SPM after a debate long time ago and now no longer available easily. Are there any particular theoretical reasons or evidences that low-pass filter should be used instead of autoregressive model or any reasons or evidences against the use of low-pass filter in rsfMRI analyses (over autoregressive model)? Or is low-pass filter used over autoregressive model just because everyone is using it?

I frequently recieve similar critiques regarding this point in different studies but I am not good at the theoretical background of these things and I hope someone can help me.
 
Hikaru