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Subject:

Re: Missing condition at first level - what to input in the onset tab?

From:

Reem Jan <[log in to unmask]>

Reply-To:

Reem Jan <[log in to unmask]>

Date:

Wed, 2 Feb 2011 16:15:32 +1300

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (197 lines)

Thank you Cyril, Michael and Donald for all the useful information you
have given me regarding this.

There are 6 runs/sessions and 4 conditions per subject.

I have done the following.

If condition A is missing from run 1 and 2 let's say.. I remove
condition A from that run (in the first level model specification), and
then in the contrast manager when I specify condition A's contrast I
type:

0 0 0 0 0.25 0 0 0.25 0 0 0.25 0 0 0.25 0 0

And if condition A is missing only from run 2:

0.25 0 0 0 0 0.25 0 0 0.25 0 0 0.25 0 0 0.25 0 0

And I also include 6 zeros after each run for the motion regressors
(which I haven't typed out here for simplicity's sake).

I hope this is right?

Kind regards
Reem

-----Original Message-----
From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]]
On Behalf Of Michael Harms
Sent: Wednesday, February 02, 2011 7:13 AM
To: [log in to unmask]
Subject: Re: [SPM] Missing condition at first level - what to input in
the onset tab?

Well, "reasonably" sure, although happy for someone else to chime in to
the contrary.  Many different contrasts can be technically valid (i.e.,
"estimable").  In the example in question, the contrast I proposed would
test for an expected difference between the mean of condition A and B of
zero, whereas in your original contrast the expected value of the
contrast would not be zero under the null hypothesis of no difference
between A and B.

In effect, I believe that by using a factor of 6/5 on the 5 A
conditions, one achieves the desired "upweighting" of the variance
associated with the condition A estimate (relative to the condition B
estimate).

cheers,
-MH


On Tue, 2011-02-01 at 13:56 +0000, Cyril Pernet wrote:
> Hi Michael
> 
> r u sure? ok beta = pinv(X)*Y so we don't really care about the bias
(I 
> was referring to nb of stimuli and variance estimation)
> 
> for the contrast with 5A and 6B using 1/5 1/6  you average 5 sessions
vs 
> 6 which of course works as well but you may want to somehow put this 
> unbalance in your contrast? I guess it's a matter of choice - note
that 
> C is unchanged by the post multiplication by inv(X'X')X'X using my 
> contrast so it is still valid too .. contrast don't have to sum up to
0 
> (oh yeh my full contrast wasn't right since I copied/pasted 6 times ..

> but I'm sure you got the gesture).
> 
> I actually never had this problem - was offering a possible solution
but 
> yours seems good too - anybody out there checked this up before?
> 
> Cyril
> 
> 
> > Hi Cyril,
> > Even if you don't have equal numbers, the estimated betas are
themselves
> > still unbiased.  Thus, if you are going to compare two conditions,
it
> > seems to me that you still want the contrast to sum to 0.  In the
example
> > you gave, if you wanted to compare the mean level of A to the mean
level
> > of B, with no estimate of A available from session 3, I think that
you
> > would want the following contrast:
> > 1/5 -1/6 0  1/5 -1/6 0  0 -1/6 0  1/5 -1/6 0  1/5 -1/6 0  1/5 -1/6 0
> > or multiplying by 6:
> > 6/5 -1 0  6/5 -1 0  0 -1 0  6/5 -1 0  6/5 -1 0  6/5 -1 0
> > (note the 0 in the "A" position of the 3rd session).
> >
> > cheers,
> > -MH
> >
> >> Reem
> >>
> >> It doesn't really matter that in each session you don't have the
same
> >> number of stimuli per condition as long as across your 6 sessions
you
> >> end up with equal numbers (will work as well if not but it's not as
good
> >> because variance estimation can be biased). As for the different
number
> >> of conditions you can weight your contrast accordingly. Simply
create 6
> >> sessions in SPM and your 2 or 3 conditions in each sessions. Let
say
> >> your design runs like this:
> >>
> >> Session 1 A B C
> >> Session 2 A B
> >> Session 3     B C
> >> Session 4 A B C
> >> Session 5 A B C
> >> Session 6 A B
> >> = 5*A 6*B 4*C
> >> =  30/6*A 36/6*B 26/6*C (I choose to have 6 as denominator because
you
> >> have 6 sessions)
> >>
> >> Let say you want to test A - B then use a contrast [30/36  -1 0
30/36
> >> -1 0 30/36  -1 0 30/36  -1 0 30/36  -1 0 30/36  -1 0]
> >> How do I end up with 30/36 and -1 -->  1/6 * 30/6 = 30/36 and -1/6
* 36/6
> >> = -1 (I use 1/6 for each session since you have 6 sessions)
> >> The sum doesn't end up to 1 but the contrast should still be valid
..
> >> (at least using a simple design on my machine with unbalanced
design and
> >> manual checking the contrast is valid - check in SPM I think it is
ok)
> >>
> >> Good luck
> >> Cyril
> >>
> >>
> >>
> >>> Dear SPMers
> >>>
> >>> I am trying run first level analysis on fMRI data in SPM8.
> >>>
> >>> The experiment design involves 6 runs/sessions, each run/session
> >>> consists of 8 stimuli (48 stimuli in total).
> >>>
> >>> There are 3 experimental conditions spread out equally across
stimuli
> >>> i.e. There are 16 stimuli of each condition in total.
> >>>
> >>> Because the stimuli were presented at random and are
event-related, I am
> >>> now running into a problem because in some of the runs of 8
stimuli,
> >>> there happens to be only 2 of the conditions presented and not 3.
In
> >>> these cases, I'm not sure what to input in the 'onsets' tab of
first
> >>> level model specification for the 3rd condition.
> >>>
> >>> Is there a way around this problem. I'd be very appreciative of
some
> >>> help.
> >>>
> >>> Kind regards
> >>> Reem
> >>>
> >>
> >> --
> >> The University of Edinburgh is a charitable body, registered in
> >> Scotland, with registration number SC005336.
> >>
> >
> >
> 
> 
 

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