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Subject:

Re: higher level analyses - using additional (behavioural) covariates

From:

Amande Pauls <[log in to unmask]>

Reply-To:

FSL - FMRIB's Software Library <[log in to unmask]>

Date:

Sun, 27 Jun 2004 17:15:02 +0100

Content-Type:

text/plain

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text/plain (181 lines)

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
>>>
>>>
>>>
>>>
>>>
>>>
>>>>I hope this makes sense. Thanks for any help.
>>>>
>>>>Amande Pauls
>>>>
>>>>--------------------------------------------------------------------
>>>>Amande Pauls
>>>>University Laboratory of Physiology, Oxford, UK
>>>>mailto:[log in to unmask]
>>>>
>>>
>>>
>>
>>--
>>--------------------------------------------------------------------
>>Amande Pauls
>>University Laboratory of Physiology, Oxford, UK
>>mailto:[log in to unmask]
>>
>
>


--
--------------------------------------------------------------------
Amande Pauls
University Laboratory of Physiology, Oxford, UK
mailto:[log in to unmask]

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