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

Satrajit Ghosh satra at mit.edu
Fri Dec 11 15:05:21 EST 2015

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



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

> 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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