Consider spectrally-matched interpolation

[tsalo]

Summary

We previously used linear interpolation, and now use cubic spline interpolation based on Nilearn, but Power et al. (2012) did their interpolation based on the spectral characteristics of the low-motion data:

In Part III, potentially compromised data were replaced after the multiple regression but prior to frequency filtering. A least-squares spectral decomposition of the uncensored (‘good’) data was performed and this decomposition was used to reconstitute data at censored (‘bad’) timepoints. To compute the frequency content of uncensored data, we applied a least squares spectral analysis adapted for nonuniformly sampled data, as described in Mathias et al. (2004),usinga method based on the Lomb-Scargle periodogram (Lomb, 1976).

I just wonder if it could help with the interpolation+temporal filtering issue we see.