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SPM  February 2011

SPM February 2011

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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 16:31:03 +1300

Content-Type:

text/plain

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text/plain (231 lines)

Oh sorry yes I meant to change that to 0.2. 

Thank you Donald

On 2/02/2011, at 16:23, "MCLAREN, Donald" <[log in to unmask]> wrote:

> The second contrast should use .2 not .25.
> 
> Best Regards, Donald McLaren
> =================
> D.G. McLaren, Ph.D.
> Postdoctoral Research Fellow, GRECC, Bedford VA
> Research Fellow, Department of Neurology, Massachusetts General
> Hospital and Harvard Medical School
> Office: (773) 406-2464
> =====================
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> 
> 
> 
> On Tue, Feb 1, 2011 at 10:15 PM, Reem Jan <[log in to unmask]> wrote:
>> 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.
>>>>> 
>>>> 
>>>> 
>>> 
>>> 
>> 
>> 
>> __________ Information from ESET NOD32 Antivirus, version of virus
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