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See below. On Sun, Jun 14, 2015 at 8:48 PM, Joelle Zimmermann < [log in to unmask]> wrote: > Hi Donald, > > Thanks for your help. Could you explain a little more? I have some > questions below: > > It's important to model ALL trials in someway or another. Thus, I would > recommend that you have 10 conditions, one for each trial. Then at the > contrast level, you can compare the 1st and 10th one. With only 1 > repetition of each trial, the estimates might not be the best. > At the contrast level - how would I set up the contrast between the 1st > and 10th trial if my suspicion is that there is more activation in trial 1 > than trial 10 for example.. could I put '1' for trial 1 and '-1' for trial > 10 and 0's for everything else? > Yes. > Importantly, where would I input my behavioural scores in this scenario? > It's important to me to be able to relate activation to behavioural > performance scores across trials. > This is a different question. Since you only have 2 trials, any difference between them will be 100% related to the behavioral difference at the single subject level. You could take the difference of the 1st and 10th to the group level and use the behavioral difference as a covariate to see if the change is related to the change in behavior. > > > The other option would be to use a parametric modulator for trial number. > The limitation of this approach is that it assumes a linear increase over > all 10 trials. Noise in the estimation of each trial isn't as much of an > issue since you are constraining the model to have an increase from trial > to trial. > Previously, I inputted my behavioural scores as values of Parametric > Modulator (my behavioural scores didn't increase linearly). The idea was > that the activation across trials changes with behavioural changes across > trials. But, didn't show me any effect. > You could use the trial number as the PM, rather than the behavioral score. This would be more similar to modeling each individual trial. > > I think the most interesting is to use the behavioural scores in my model. > I am just not sure how, since my previous analysis did not show me an > effect. That is why I am thinking to narrow down to comparison to trial 1 > and trial 10 (because there should be a big change there even if there > isn't from trial to trial). > You can't model each trial separately and use the behavioral score as a covariate at the individual model level. You can only relate trial10-trial1 and the change in behavior at the group level. > > I appreciate your help. > > Thanks, > Joelle > > > On Sun, Jun 14, 2015 at 10:11 PM, MCLAREN, Donald < > [log in to unmask]> wrote: > >> Joelle, >> >> It's important to model ALL trials in someway or another. Thus, I would >> recommend that you have 10 conditions, one for each trial. Then at the >> contrast level, you can compare the 1st and 10th one. With only 1 >> repetition of each trial, the estimates might not be the best. >> >> The other option would be to use a parametric modulator for trial number. >> The limitation of this approach is that it assumes a linear increase over >> all 10 trials. Noise in the estimation of each trial isn't as much of an >> issue since you are constraining the model to have an increase from trial >> to trial. >> >> Since you've already done the second way, you can try the first way. The >> lack of repetition of each trial may make it hard to find significance of a >> difference over trials, if there is an increase. >> >> Best Regards, Donald McLaren >> ================= >> D.G. McLaren, Ph.D. >> Research Fellow, Department of Neurology, Massachusetts General Hospital >> and >> Harvard Medical School >> Postdoctoral Research Fellow, GRECC, Bedford VA >> Website: http://www.martinos.org/~mclaren >> Office: (773) 406-2464 >> ===================== >> This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED >> HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is >> intended only for the use of the individual or entity named above. If the >> reader of the e-mail is not the intended recipient or the employee or >> agent >> responsible for delivering it to the intended recipient, you are hereby >> notified that you are in possession of confidential and privileged >> information. Any unauthorized use, disclosure, copying or the taking of >> any >> action in reliance on the contents of this information is strictly >> prohibited and may be unlawful. If you have received this e-mail >> unintentionally, please immediately notify the sender via telephone at >> (773) >> 406-2464 or email. >> >> On Sun, Jun 14, 2015 at 6:24 AM, Joelle Zimmermann < >> [log in to unmask]> wrote: >> >>> Hi - I'm writing with the hopes that somebody can give me advice about >>> how to formulate a particular SPM analysis I want to do (let's start with >>> first-level). >>> >>> My data: >>> >>> - 10 trials (where subjects have fMRI measurement and perform >>> behavioural task). I have a single behavioural performance score per trial >>> for a subject. >>> - in between the trials are short 'rest' periods >>> >>> My goal: >>> >>> - Compare first and last trial - to see whether activation changes >>> between first trial and last trial underlie changes in behavioural >>> performance score between first and last trial (there is indeed an increase >>> in behavioural score from first to last trial). >>> >>> My idea is to set up each of the 10 trials as a 'condition'. >>> Alternatively, set up perhaps only the first trial as a condition, and the >>> last trial as a condition. Where in the design can my behavioural scores >>> for the first trial and the last trial go? >>> >>> Additional info: >>> >>> - I've previously ran an analysis, setting up one 'condition', with >>> 10 onsets (ie the trial onsets), one Parametric Modulator (with the 10 >>> behavioural scores as 'values'). I set up a t-contrast, where I looked at >>> only the second column (ie my Parametric Modulator) such as 0 1 0 ... >>> - I did not get any significant voxels this way. >>> - This is why I am thinking of only comparing 1st and last trial >>> (rather than all 10 trials). >>> >>> >>> Any pointers would be very helpful. >>> Thanks, >>> Joelle >>> >>> >> >