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Hi there - > > what I was thinking of is a partial correlation I think. I am interested > in the variance I get from the additional covariate (B) in areas that > are active in my experimental task (A). OK, you can get this by orthoganalising B wrt A - this will tell you the aditional signal variance explained by B which cannot be explained by A. You can then use contrast masking, to only show these results in areas where the effect of A is significant. The assumption here is that my > additional covariate (B) modulates the extent to which (A) activates the > brain and I'm interested in that component rather than wanting to get > rid of it. This is now a slightly different question. To look at the modulation of A by B you would need to look at the interaction between A and B. This tells you the effect on the response to stimulus A of covariate B. (or vice versa) > > Concerning the orthogonalisation, I am a bit confused as I don't seem to > have the option of making the EVs orthogonal with respect to each other > in the higher level analyses. Is there another way of doing it? Also, is > the assumption that EVs are or are not orthogonal with the current > higher level Feat options? > No you're right, you can't do this at present, but you will be able to in the next release of Feat. For now, you can perform the orthogonalisation in e.g. matlab. to orthogonalise B wrt A in matlab, do e.g. B_orth=B-A*pinv(A)*B Cheers T > Thanks a lot, > Amande. > > > > Tim Behrens wrote: > > > Hi Amande, > > > > I'm afraid I still don't understand what you want to do, > > > > What do you mean by " a correlation with B _given_ A " ? > > > > Is it a partial correlation > > > > " a correlation of the data with B accounting for the effects A " ? > > > > or a multiple regression > > > > "How much of the variance in the data can a prescribe to both A and B > > together?" > > > > I'm sure there will be a simple way of answering your question. The GLM is > > amazingly flexible. I just can't see exactly what the question is yet.. > > > > > > wrt non-orthogonal EVs > > If your EVs are not orthogonal, then any shared variance will be dished > > out between them in a way which is not easily predictable a priori. > > However, if you are comparing conditions, this shared variance is > > accounted for in the statisitics, so you can still accurately answer the > > question "Where is the response to A bigger than that to B?" for example. > > > > Sorry I'm not more use > > > > Tim > > > > > > > > On Sun, 27 Jun 2004, Amande Pauls wrote: > > > > > >>Hi, > >> > >>no, what I was wondering about is whether there is any way I can test > >>whether there is a correlation with B (behavioural covariate) given A > >>(task), e.g. by not making the EVs orthogonal. So really what I want to > >>know is what it means if the two EVs are not orthogonal and whether this > >>is in any way statistically meaningful. Such as activation in a certain > >>area given RT on the task, or activation in a task given IQ or something. > >> > >>Amande > >> > >> > >> > >>Tim Behrens wrote: > >> > >> > >>>... > >>> > >>>"At which voxels does my signal contain variance which can be explained by > >>>my RTs _but not_ by my task EV ?" > >>> > >>>sorry - this might be confusing. > >>> > >>>more accurate is > >>> > >>>"At which voxels does my signal contain variance which can be explained by > >>>my RTs after accounting for variance explained by my task EV ?" > >>> > >>> > >>> > >>>------------------------------------------------------------------------------- > >>>Tim Behrens > >>>Centre for Functional MRI of the Brain > >>>The John Radcliffe Hospital > >>>Headley Way Oxford OX3 9DU > >>>Oxford University > >>>Work 01865 222782 > >>>Mobile 07980 884537 > >>>------------------------------------------------------------------------------- > >>> > >>>---------- Forwarded message ---------- > >>>Date: Wed, 23 Jun 2004 16:16:33 +0100 (BST) > >>>From: Tim Behrens <[log in to unmask]> > >>>To: FSL - FMRIB's Software Library <[log in to unmask]> > >>>Subject: Re: [FSL] higher level analyses - using additional (behavioural) > >>> covariates > >>> > >>>Hi Amande - I'm not sure whether I've understood this right, but it sounds > >>>like what you want is exactly the opposite of the previous scenario. That > >>>is > >>> > >>>"At which voxels does my signal contain variance which can be explained by > >>>my RTs _but not_ by my task EV ?" > >>> > >>>If this is the case, you want to run the orthoganisation the other way > >>>round. That is, you want to orthoganalise the RTs wrt the task covariates. > >>> > >>>This will remove from the RT EV, any variance which could be explained by > >>>the task. > >>> > >>>Hope this is what you want > >>> > >>>T > >>> > >>> > >>> > >>> > >>> > >>> > >>>On Wed, 23 Jun 2004, Amande Pauls wrote: > >>> > >>> > >>> > >>>>Hi there, > >>>> > >>>>thanks for the quick reply. > >>>> > >>>>I have another question about point (3). Say I don't want to factor out > >>>>the additional covariate, but want to see whether there are areas whose > >>>>variance reflects the additional covariate given the task. An example > >>>>would be some motor task and I want to 'rank' people according to RTs > >>>>I've got from them in some other task (or rather see whether activity in > >>>>this task somehow reflects their prior motor performance). Do I use RTs > >>> > >>>>from the other task as additional covariate and then don't make the EVs > >>> > >>>>orthogonal? Or do I have to set that up in the contrasts somehow, after > >>>>they have been orthogonalised? > >>>> > >>>>What I would like to know is whether my additional covariate, of > >>>>interest or not, will correlate only with areas activated by the task, > >>>>or whether what I see could also reflect baseline activity in some > >>>>resting state network (like language areas etc). > >>>> > >>>>Thanks again. > >>>>Amande > >>>> > >>>> > >>>> > >>>> > >>>> > >>>>Tim Behrens wrote: > >>>> > >>>> > >>>>>Hi there > >>>>> > >>>>>On Tue, 22 Jun 2004, Amande Pauls wrote: > >>>>> > >>>>> > >>>>> > >>>>> > >>>>>>(1) Is it possible to control for an additional covariate that I am not > >>>>>>interested in but suspect to have an influence on the outcome of the > >>>>>>experiment (like measures of intelligence)? By modelling all of them as > >>>>>>an additional EV? Or by making it one per subject (like when allowing > >>>>>>for individual differences in variance)? > >>>>> > >>>>> > >>>>>Yep - you should use a single EV for each covariate of no interest (e.g. > >>>>>one EV for IQ, ine for age etc. etc. ) > >>>>> > >>>>> > >>>>> > >>>>> > >>>>>>(2) If I want to know whether there is a negative correlation between my > >>>>>>additional covariate (modelled as a separate EV) and the data, do I need > >>>>>>to set the contrast to -1? > >>>>>> > >>>>> > >>>>> > >>>>>Yes - absolutely right. > >>>>> > >>>>> > >>>>> > >>>>> > >>>>>>(3) Having used an additional covariate (like the RT example in the > >>>>>>webpages) and using that precise contrast, what does the result mean? > >>>>>>I'm unclear on whether the brain area correlates both with RT and the > >>>>>>task itself, or whether the level activity in that area somehow reflects > >>>>>>RT, potentially independent of the task. Can I distinguish between those > >>>>>>two cases, or make sure that my contrast reflects 'correlation with > >>>>>>EV, given the task'? > >>>>>> > >>>>> > >>>>> > >>>>>It depends on the precise setup that you have chosen, but if you > >>>>>orthogonalise such that RT explains the maximum possible variance (i.e. > >>>>>orthogonalise all covariates of interest wrt RT) then the RT contrast > >>>>>represents all the variance in the signal which _might possibly_ be > >>>>>explained by RT. The copes of interest cannot then describe any variance > >>>>>in the signal which could be ascribed to RT. > >>>>> > >>>>>JHupw this is clear > >>>>> > >>>>>Tim > >>>> > > ------------------------------------------------------------------------------- > > Tim Behrens > > Centre for Functional MRI of the Brain > > The John Radcliffe Hospital > > Headley Way Oxford OX3 9DU > > Oxford University > > Work 01865 222782 > > Mobile 07980 884537 > > ------------------------------------------------------------------------------- > > > > > -- > -------------------------------------------------------------------- > Amande Pauls > University Laboratory of Physiology, Oxford, UK > mailto:[log in to unmask] > -- ------------------------------------------------------------------------------- Tim Behrens Centre for Functional MRI of the Brain The John Radcliffe Hospital Headley Way Oxford OX3 9DU Oxford University Work 01865 222782 Mobile 07980 884537 -------------------------------------------------------------------------------