Thanks for your responses, that's helped clarify a lot of points! David
On 21 January 2014 11:15, Brian Murphy <[email protected]> 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 > [email protected] > > > > _______________________________________________ > Pkg-ExpPsy-PyMVPA mailing list > [email protected] > http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa >
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