 |
 |
Hi Mark, I think this is important to note. So do you see any problems in applying FLOBS or filmbabe at the first level with regards to higher-level stats? Cheers- Andreas -----Ursprüngliche Nachricht----- Von: Mark Woolrich [mailto:[log in to unmask]] Gesendet: Mi 24.11.2004 12:49 An: [log in to unmask] Cc: Betreff: Re: [FSL] FLOBS? Serge, >I am looking for a method to test for differences in the BOLD-time course >between two groups. Ideally, I would like to test for differences in time >to peak, full width half maximum, time to onset etc. >Perhaps FLOBS can be used to do this. Unfortunately, not easily. You probably have in mind the parameterised HRF made up of half cosines upon which FLOBs is based. However, FLOBS only uses this parameterised HRF to generate a linear basis set. Hence any explicit parameterisation of time to peak etc. is lost. It is possible to recover the time to peak etc. by extracting them from the inferred basis set impulse response function (IRF) (e.g. finding the time to peak for the smoothed basis set IRF is not difficult). >I have one condition only: when I >use FLOBS using the default functions, I can ask for 3 contrasts (one for >each regressor) and for the F-test giving me a linear combination of the >3 functions. >My question now is: how can I use these FLOBS results at second level? >Are there ways to compare the best fit of the BOLD response between group >A and B? >Is there perhaps a way to extract measures such as time to peak, fwhm >etc, and then use these at second level? This relates to a general problem that exists when we try to use basis sets with group studies. That is, if we pass up the COPEs for each of the basis functions to a second level, and then look for differences between groups of subjects, then we could get a significant difference not only for a difference in size of effect, but also for a difference in shape of effect. There is no simple way to separate out this two possiblities using basis functions. Instead, you would need to recover explicit estimates of size, time to peak etc. from the inferred basis set IRF from the output from the first level (see above) and then pass them to the second level. There is then the question of whether you can assume gaussianity at the second level for these sorts of parameters, I have no experience of this - so couldn't say definitively. If they are Gaussian then you can do a traditional OLS random effects analysis, if not then you could still use nonparametric stats. Cheers, Mark. Mark Woolrich. Oxford University Centre for Functional MRI of the Brain (FMRIB), John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK. Tel: (+44)1865-222782 Homepage: http://www.fmrib.ox.ac.uk/~woolrich