[Neuroimaging] Effects of motion outliers on HRF model (in sparse acquisition fMRI)

[Satrajit Ghosh]

hi chris,

this is a standard sparse design and you can use the sparse model in nipype
to get at events and amplitudes that you feed into standard SPM/FSL
designers. the key is to not use a canonical HRF in the modeling stage.

we have a standard openfmri (not BIDS yet) sparse script that can do the
entire preprocessing and estimation on such data and would be happy to
share.

cheers,

satra

On Fri, Dec 11, 2015 at 2:43 PM, Christopher J Markiewicz <effigies at bu.edu>
wrote:

> Hi all,
>
> I apologize in advance because, as Pythonic as my pipeline is, my issue
> here isn't really Python-related. However, the people on this list are
> the most likely to have dealt with similar issues (of places I know to
> look). If you'd rather I post on NeuroStars, I can, but I'm not sure how
> much people are actually using that.
>
> Anyway, my functional data comes from evenly-spaced, sparse acquisitions
> (TA=2.25s, TR=3.375s), and I've used artdetect in nipype to tag motion
> and intensity outliers. It's a fast, event-related design (one event
> every 2 TRs).
>
> In the past, my strategy has been to estimate HRF betas on the full
> dataset, and then excluding motion outliers in analysis by removing any
> event estimate that had an above-threshold contribution from an outlier
> volume. That is, in an NxM design matrix estimating N events from M
> scans, if scan j is an outlier, we exclude all events i such that
> DM[i,j] > (e.g.) 10% of max(HRF).
>
> Another strategy I'm looking into is to add nuisance regressors for
> outlier volumes to the design matrix, and limiting bleed-over into
> unrelated events. This is running into problems with "runs" of outliers,
> which can leave some events with nothing by which to estimate or only
> very small contributions from volumes that are going to be dominated by
> other events. I could remove such events, entirely, but for various
> reasons (mostly involving maintaining ordering so that off-by-one errors
> don't slip into our analysis) I'd like to have some representation of
> each event.
>
> The best external-to-our-lab resource I could find was this Gabrieli Lab
> protocol
> (
> https://github.com/gablab/mindhive/wiki/Example-of-sparse-fMRI-data-analysis-using-BIPS
> ),
> which seems to indicate they've included the full dataset and noted
> outliers after estimation.
>
> Does anybody have any experience with outlier exclusion at or before HRF
> estimation, or is this the current best practice?
>
> Thanks,
> --
> Christopher J Markiewicz
> Ph.D. Candidate, Quantitative Neuroscience Laboratory
> Boston University
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> Neuroimaging at python.org
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