Hello AFNI minds,
So since the beginning, I’ve always concatenated runs of a multi-run task (usually two) very early in pre-processing, then I use cumulative times in the 3dDeconvolve modeling, as well as the -runs specifier and invoked text file with 3dDeconvolve, so that (IIRC) 3dDeconvolve will model out the run effects.
What this does is enables the output of the 3dVolReg command (performed on the entire concatenated time series) to more truly show how the subject’s head moved in space across the entire experience of performing the task. I would include the motion parameter outputs in the 3dDeconvolve modeling, to try to control for residual or uncorrected motion. After 3dvolreg and smoothing, I would use align_epi_anat.py to align the MP-RAGE structural to this concatenated functional time series.
Recently, however, I had a case where the subject shifted his head markedly after the first run, where the last two (of three) runs were generally pretty stationary. Moreover, even across the first run, the motion was pretty minimal. This was all evident because I ran 1dplot on the output of 3dVolReg of the concatentated time series. My traditional way still yielded a large head-shift that was also evident visually when I cycled the entire time-series in the afni underlay viewer.
On a lark, I re-did the pre-processing, only this time, I first ran align_epi_anat.py for each task run singly to individually align each task run EPI to the one MP-RAGE, using the -epi2anat operation, and allowed the 3dVolReg to take place as part of this script, instead of as a stand-alone command. Then I concatenated these aligned EPI files, and also the three text outputs of the volume correction. The result was a timeseries that appeared smoother and stiller when cycling across the entire timeseries. the 1dplotted concatenated motion now showed sub-mm motion all throughout. Wow! or so I thought.
So I ran the task regression model on both timeseries. the beta weights for the modeled motion parameters were on average higher for my orginal-way than for my new reverse-aligned data, which would make sense if revised way controlled for motion better. However, not all motion beta weights were lower in my revised preproccessing. Moreover, instead of seeing perhaps a more cleaned up and more statistically signitifcant first-level map of task activation. the two statistical maps look rather different, where my original timeseries actually showed more canonical activations than my revised. I honestly don’t know which is “truth” or whether to pitch the whole scan session.
So my question is, is there something inherent to the way 3dDeconvolve works that would make my recent runwise -epi2ant strategy more (or less) effective in cases like this? I’m just so struck at how different the magnitudes and patterns of task activations are. Is there a better way to handle cases where there is a large inter-run shift, but minimal head motion within run? It just seems to me that what I did is akin to having a two-session experiment within-subject, like dose vs placebo day, and you have to align, and maybe you only had time to get the MP-rage on one of the sessions…