Hi Tim,
I will try out the orthogonalisation. Can you roughly tell me what
happens at the moment when I use more than one EV - I am assuming they
are not orthogonal so unless I complete the extra step in matlab the
results are uninterpretable?
>
> 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)
And that's not possible at the moment I take it? Is there any workaround?
Sorry to be so difficult, thanks for all the help.
Best,
Amande
>
>>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
> -------------------------------------------------------------------------------
>
--
--------------------------------------------------------------------
Amande Pauls
University Laboratory of Physiology, Oxford, UK
mailto:[log in to unmask]
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