[pymvpa] Accounting for the haemodynamic lag

[David Watson]

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
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