Wednesday, October 19, 2016

note on the subcortical part of CIFTIs

As people working with HCP CIFTI images know, the cortex is represented by (surface) vertices, but subcortical structures as voxels. As I explained before, you can use the wb_command -cifti-separate to make a volumetric NIfTI of a subcortical structure (say AMYGDALA_LEFT). The HCP minimally preprocessing pipelines create the CIFTI versions of each (task, in my case) run and also volumetric NIfTI versions. So my question: is timecourse of a voxel in a subcortical structure the same when taken out of an unpacked CIFTI and a NIfTI? Answer: no.

This screenshot shows the volumetric NIfTI (red stripe),
 AMYGDALA_LEFT via wb_command -cifti-separate (green stripe), and the _AtlasSubcortical_s2.nii.gz image (yellow stripe) for one of our HCP pipeline minimally-processed task fMRI runs.

The _AtlasSubcortical_s2.nii.gz image was created by the pipeline, and looks to be the subcortical (volumetric) part of the CIFTI: the value at this voxel is identical (green and yellow). The value for the voxel in the volumetric NIfTI (red) is a bit different, however.

Note that I don't think this mismatch is an error: parcel-constrained smoothing is done during the HCP pipelines that make the CIFTIs (e.g., Glasser et al. 2013), and I suspect that this smoothing accounts for the different voxel values. However, it's good to be aware of the additional processing: if you're doing a volumetric analysis with HCP-type preprocessing, it matters whether you take the subcortical structures out of the CIFTI or the NIfTI.

Friday, October 14, 2016

an update and some musings on motion regressors

In case you don't follow practiCal fMRI (you should!), his last two posts describe a series of tests exploring whether or not the multiband (AKA simultaneous multi-slice) sequence is especially liable to respiration artifacts: start here, then this one. Read his posts for the details; I think a takeaway for us non-physicists is that the startlingly-strong respiration signal I (and others) have been seeing in multiband sequence timecourses and motion regressors is not from the multiband itself, but rather that respiration and other motion-type signals are a much bigger deal when voxels are small (e.g., 2 mm isotropic).

This week I dove into the literature on motion regression, artifact correction, etc. Hopefully I'll do some research blogging about a few papers, but here I'll muse about one specific question: how many motion regressors should we use (as regressors of no interest) for our task GLMs? 6? 12? 24? This is one of those questions I hadn't realized was a question until running into people using more than 6 motion regressors (the 6 (x,y,z,roll,pitch,yaw) come from the realignment during preprocessing; transformations of these values are used to make the additional regressors).

Using more than 6 motion regressors seems more common in the resting state and functional connectivity literature than for task fMRI (Power et al. 2015, and Bright & Murphy 2015 , for example). I found a few (only a few) task papers mentioning more than 6 motion regressors, such as Johnstone et al. 2006, who mention testing "several alternative covariates of no interest derived from the estimated motion parameters", but they "lent no additional insight or sensitivity", and Lund et al. 2005, who concluded that including 24 regressors was better than none.

Out of curiosity, we ran a person through an afni TENT GLM (FIR model) using 6 (left) and 24 (right) motion regressors. This is a simple control analysis: all trials from two runs (one in blue, the other orange), averaging coefficients within my favorite left motor Gordon parcel 45 (there were button pushes in the trials). It's hard to tell the difference between the model with 6 and 24 regressors: both are similar and reasonable; at least in this test, the extra regressors didn't have much of an effect.

My thinking is that sticking with the usual practice of 6 regressors of no interest is sensible for task fMRI: adding regressors of no interest uses more degrees of freedom in the model, risks compounding the influence of task-linked motion, and hasn't been shown superior. But any other opinions or experiences?

Saturday, October 1, 2016

multiband acquisition sequence testing: timecourses 2

In a previous post I showed timecourses from the same person, doing the HCP MOTOR task, collected with a multiband 4 and a multiband 8 sequence (see previous posts for details). We ran another test person ("999010") through the same task, with the same two sequences (but not the MB0 control).

This plot shows the average activation (post-preprocessing) in the same SMmouthL Gordon parcel as before, but with the respiration recording as background (light blue), instead of the motion regressors. As before, since this is a mouth parcel, the activation should be strongest to the green "tongue" movement blocks. The respiration recording, events, and average activation are temporally aligned to the actual TR (x-axis), not shifted to account for the hemodynamic delay.

It is clear in both the MB8 and MB4 recordings that task activation is present, but it is perhaps a bit clearer with MB4. The little "jiggles" in the timecourse are present in all four runs, and look to be related to respiration, though not perfectly aligned with respiration. We're switching our ongoing MB8 imaging study to MB4 in the hopes of improving signal-to-noise, but respiration-related effects still look to be prominent in the MB4 BOLD, so dealing with them is an ongoing project.