Using HCP minimal preprocessed fMRI data for White matter FC analyses

[Brandon Taraku]

Dear HCP experts,

There has been recent interest among certain research groups to look at BOLD signal in white matter (WM). This paper highlights some of the relevant findings, as well introduces a toolbox to perform preprocessing and analyses tailored to white matter fMRI.

However, since I have HCP minimally preprocessed data, I was wondering it would be better to use that data with HCP preprocessing instead of following their preprocessing steps. From the paper, it seems they mainly use PCA (CSF components) and motion-regression based preprocessing steps as opposed sICA+FIX used in HCP.

Do you think HCP minimal preprocessing steps would be appropriate for looking at BOLD in WM or do you think it would be better to follow their preprocessing pipeline? Or do you think I should run their preprocessing steps on top of the HCP minimally preprocessed data?

Thank you for the advice,

Brandon

[Glasser, Matthew]

We’re skeptics on this topic.  The main WM BOLD signal is removed in the HCP Pipelines in the sICA+FIX step.  This is largely delayed global respiratory artifact (the white matter vasculature is delayed relative to the cortical vasculature, which enables sICA to separate global respiratory artifact as a separate, non-global spatial ICA component).

 

It’s also worth noting that the effects of grey matter BOLD extend pretty far into the white matter (see Jonathan Power’s work).   You really have to erode a couple of voxels in to eliminate it, and thus you are only left with a few deep white matter voxels (probably less than ~30% of the white matter voxels).

 

Matt.

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