more motion and activation plotting

After my initial "what is this?" post I've become convinced that the oscillations are due to respiration, both from my own data and others' reports. I'll leave it to the physicists to understand why the patterns are especially prominent in these sequences, but I've been trying to understand their impact for our work: we're collecting task data and want to do event-related and FIR analyses. Is the oscillation something superimposed on (but independent of) our real signal such that we can "ignore" or process it out (maybe with signal separation techniques such FIX ICA) after image collection, or is our event-related BOLD getting lost, and we should change the acquisition parameters?

These images show some of the plots I've been playing with to try evaluating those questions, using HCP data for comparison. The x-axis is frame number, with thick vertical grey lines marking one-minute intervals and thin vertical grey lines marking 30-second intervals. The left-side y-axis is translation in mm (from the Movement_Regressors.txt file), while the right-side y-axis is the average BOLD of the voxels in each parcel (which I generated via afni; details below; these are the minimally preprocessed images). Note that the left y-axis (translation) is fixed in all graphs while the right-side axis (activation) varies.

The first three columns of Movement_Regressors.txt are plotted in light green, light blue, and light red (in that order), with the average activation overlaid in black (right-side axis). The dark blue line that tracks the activation is a lowess smooth. I generated these plots for four HCP people, in each of four Gordon parcels (4, 41, 45, 201), and for six task runs (MOTOR, GAMBLING, and EMOTION, RL and LR of each). I chose those task runs because they all involve hand motion, blocked (MOTOR) or as event responses (GAMBLING and EMOTION). The block or button-push time is shown in the graphs as little tick marks along the x-axis, with R and L marking the right and left-hand block onsets for MOTOR. I picked four parcels of similar size; 45 is in the left-hemisphere and 201 in the right-hemisphere SMhand communities, and should show strong hand-motion-related activation.

Plots for all four people, runs, and parcels are in this pdf, with two sets copied in here. These are plots for the four parcels, the MOTOR_RL run only. The purple lines show when we expect hand-motion task activation to be strongest for parcels 45 and 201, and it is, particularly in the smoothed line (good, signal!). The oscillation is also clearly visible, and related (by my eye, at least) to the pattern in the motion regressors. There are also spikes, some of which I marked with red dots. It's a bit hard to see in these plots, but these spikes strike me as aligned to jags in the motion regressor lines (and presumably, respiration).

Looking at a lot of these plots makes me understand why I've had trouble (in the other dataset as well) with single-TR and short temporal-compression analyses (e.g., averaging 4 or 5 TRs after an event): I'm picking up too much of the oscillation. Signal clearly is present here, but trying to detect it for single (or short) events is proving tricky.

(example afni command after the jump)
Here's an example afni command for extracting the average timeseries from a single parcel:

 3dmaskave -mask Gordon.nii -mrange 3.5 4.5 /900subject/162026/MNINonLinear/Results/tfMRI_MOTOR_LR/tfMRI_MOTOR_LR.nii.gz > out.txt  

Gordon.nii is the Gordon parcellation, from which I extracted (in this example) parcel 4, and out.txt is the text file with the timeseries I put into the plots.

UPDATE 2 September 2016: To extract from all parcels, it's easier with 3dROIstats:

 ./3dROIstats -mask /Gordon.nii functional.nii.gz > out.txt