## 4 Discussion
However, we did see that in Subject #5 the motion effects could not be completely resolved with AMARA-fMRI to reveal the underlying neuronal activation shown with fMRI. This subject had a very high amount of motion in all channels, and although motion correction increased t-statistics in deoxyhemoglobin, it did not meet the significance threshold when corrected for multiple comparisons. This does suggest limitations on the amount of motion that can removed; however, these limitations are relevant only with very strong motion artifacts. Besides these limitations, there are also additional limiting factors to our method. Namely, since our method relies on extracting the maximum amount of motion information possible from the fMRI data by measuring motion in sub-timepoints of the TR, our method will fail in cases where (1) the fMRI acquisition method does not allow for three-dimensional sub-TR motion estimation, for example if a non-multiband acquisition is used; (2) the multiband factor is too low to get good motion estimates from the subsampled data (the slice stacks are too coarse to estimate motion reliably); or (3) the multiband factor is too high, so the degree of subsampling within a TR is too low to give an effective boost to the sample rate. Lastly, these results are based on a very small number of channels, making this method well adapted to multimodal experiments in which only a small number of channels are used due to the set-up time, subject comfort, and head position when lying down among other reasons. Motion correction methods based on global temporal filtering may perform poorly in these instances. Though these are very promising results, we did not observe a significant improvement in the oxyhemoglobin activations. This is most likely due to the higher initial signal-to-noise ratio of oxyhemoglobin; in voxels with significant activation, the signal was most likely already detected, so improved motion correction would not be expected to make a large difference (Figure 3e). There may also be an influence attributable to the window length and step size chosen. AMARA was originally designed for extremely long free-moving studies, with window length and step size of 15 min and 5 min, respectively. The multimodal fMRI studies are restricted in terms of their motion and are of much shorter duration (e.g., commonly used paradigm times of approximately 10 min), so we would expect them to require significantly different tuning parameters than the original reference. We arrived at the chosen values using an exhaustive hyperparameter search on a single subject in a preliminary study [39]. Incidentally, we saw here that a higher window length of 10, with step size of 5 min, was more advantageous for oxyhemoglobin. While the values we selected are clearly effective at improving the power of task detection in an existing dataset, repeating the procedure in a larger, more heterogeneous dataset encompassing more subjects and a variety of tasks will be required to tune these values optimally. However, this was beyond the scope and power of the current study. Conceptualization, L.M.H. ; methodology, L.M.H. ; software, L.M.H. ; validation; formal analysis, L.M.H. ; investigation, L.M.H. ; resources, L.M.H. and B.d.F. ; data curation, L.M.H., Y.T. and B.d.F. ; writing--original draft preparation, L.M.H. ; writing--review and editing, L.M.H., B.d.F. and Y.T. ; visualization, L.M.H. ; supervision, L.M.H. and B.d.F. ; project administration, L.M.H. and B.d.F. ; funding acquisition, L.M.H. and B.d.F. All authors have read and agreed to the published version of the manuscript. This research was funded by NIH/NIA under grant number R21 AG070383-01 (LMH) and NIH/NIH under grant number RF1 MH130637-01 (BBF). The data and results of this paper are not shared since consent was not given for sharing data and was not requested in the original IRB. The code to transform the motion parameters of multislice imaging into high-resolution motion time courses is available as open software by [PERSON] (2018).