[pymvpa] Accounting for the haemodynamic lag

[David Watson]

Thanks for your responses, that's helped clarify a lot of points!

David


On 21 January 2014 11:15, Brian Murphy <brian.murphy at qub.ac.uk> wrote:

> Hello David,
>
> you have a few options. You can aim to get a single aggregate volume for
> each trial, by a) assuming a standard HRF corresponds well to the
> responses seen in your data, and use that to do a weighted average of
> your trial volumes (NiPy has a built in HRF); or b) do a simple block
> average as you suggest (e.g. taking an offset of 2 TRs, and block
> averaging the following couple).
>
> You could also do either of those things with a cross-validated
> parameter setting step (to decide on the optimal offset and block
> length, or HRF parameters) - keeping in mind that the HRF can vary by
> individual participant, task and brain area (the 'standard' HRFs are
> based on low level visual cortex responding to flashes of light).
>
> If your main interest is getting good classification results, you could
> also throw in all the volumes together (each trial would be comprised by
> V voxels x T TRs), and let the machine learning methods decide which
> volumes (or weighting thereof) are the ones it wants to listen too. The
> advantage there is that there you make no assumptions at all about the
> timing and shape of the response, and you don't assume a uniform
> response across people/locations/tasks. With that approach you'll need a
> classifier that works well with large numbers of co-linear dimensions -
> e.g. PLR or Random Forests.
>
> We wrote a paper covering some of that ground which you can look at for
> background:
> http://www.frontiersin.org/Journal/10.3389/fninf.2012.00024/abstract
> ... and I can dig out the associated code if that is helpful,
>
> best of luck,
>
> Brian
>
> On Mon, 2014-01-20 at 09:55 +0000, David Watson wrote:
> > Dear All,
> >
> > I was wondering if anyone could give me some advice on how best to
> > account for the haemodynamic lag of the BOLD signal when performing a
> > pattern analysis on 4D fMRI data?  This seems like a fairly basic
> > issue, but I am struggling to find a clear answer on how best to deal
> > with it. I have spent some time reading around (e.g. this page from
> > the Princeton toolbox was quite informative:
> >
> http://code.google.com/p/princeton-mvpa-toolbox/wiki/HowtosRegressors#How_can_I_take_the_haemodynamic_lag_into_account)
> and I get the impression that there are two main ways that people tend to
> do this:
> >
> > 1. Offset the timeseries or the sample labels by a suitable number of
> > TRs. For instance, my TR is 3 seconds, and the lag is estimated to be
> > approximately 6 seconds for most subjects, so I could either remove
> > the first 2 TRs of the timeseries, or increment my sample labels along
> > 2 time points. I could easily enough do this myself within python once
> > I've loaded in my sample attributes and dataset, although maybe PyMVPA
> > already has some built in support for this function that I have
> > missed. But I am a little concerned as to how accurate this is likely
> > to be, e.g. the lag is unlikely to be precisely 6 seconds in all
> > subjects.
> >
> > 2. Convolve my model regressors with an HRF. This option seems like it
> > might be preferable, and I can easily enough derive a gamma HRF (e.g.
> > the nitime package seems to provide one), but I'm not sure how I would
> > then apply this to a given model within PyMVPA. Or does PyMVPA already
> > provide some functionality to let me do this?
> >
> > As it happens I have a block design so perhaps I could get away with
> > just offsetting the timeseries, although convolving an HRF might still
> > be preferable. But if I ever wanted to do an event-related design
> > where measuring timings precisely is more important then I'm not sure
> > if simply offsetting the timeseries would still be considered
> > acceptable. Also, are there any other commonly used methods of
> > accounting for the lag that I have missed?
> >
> >
> > Regards,
> >
> > David
> >
> >
>
> --
> Dr. Brian Murphy
> Lecturer (Assistant Professor)
> Knowledge & Data Engineering (EEECS)
> Queen's University Belfast
> brian.murphy at qub.ac.uk
>
>
>
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