Email discussion lists for the UK Education and Research communities

## SPM@JISCMAIL.AC.UK

#### View:

 Message: [ First | Previous | Next | Last ] By Topic: [ First | Previous | Next | Last ] By Author: [ First | Previous | Next | Last ] Font: Proportional Font

#### Options

Subject:

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

From:

Michael Harms <[log in to unmask]>

Reply-To:

Michael Harms <[log in to unmask]>

Date:

Tue, 1 Feb 2011 12:12:55 -0600

Content-Type:

text/plain

Parts/Attachments:

 text/plain (116 lines)
 ```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. > >> > > > > > > ```

#### RSS Feeds and Sharing

JiscMail is a Jisc service.

View our service policies at https://www.jiscmail.ac.uk/policyandsecurity/ and Jisc's privacy policy at https://www.jisc.ac.uk/website/privacy-notice