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Hi Donald, Thanks so much! A couple of follow up questions are indicated in my inline responses below. Cheers, Daniel On Thu, Dec 12, 2013 at 9:29 PM, MCLAREN, Donald <[log in to unmask]>wrote: > Daniel, > > Please see inline responses below. > > > On Wed, Dec 11, 2013 at 10:12 AM, Daniel Weissman <[log in to unmask]>wrote: > >> Dear SPMers, >> >> I'd like to run an event-related fMRI study to investigate the neural >> bases of sequential effects involving two trial types: A and B (each trial >> lasts 2 seconds and involves a stimulus for 300 ms, followed by the >> subject's response). To this end, I'd like to employ a first-order >> counterbalanced sequence to produce equal numbers of four sequential trial >> types. >> >> 1) A preceded by A >> 2) A preceded by B >> 3) B preceded by A >> 4) B preceded by B >> >> Finally, I'd like to use a constant inter-trial-interval (ITI), because >> sequential effects in my task will likely change with the ITI. This is a >> bit different from my normal practice of jittering the ITI and so it raises >> a few questions in my mind. >> >> 1. Although the absence of jitter will make it nearly impossible to >> observe the common effect of all conditions vs. baseline, my understanding >> is that I will still be able to perform contrasts involving the 2 main >> trial types (e.g., A minus B) as well as the 4 sequential trial types >> (e.g., A preceded by A minus A preceded by B). This is because differences >> between beta values are relatively stable when no jitter is present, even >> though the individual beta values themselves are highly unstable. Is this >> correct? >> > > The absence of jitter reduces the ability to determine the amplitude of > the events because you can lead to an underdetermined model and unstable > estimates. However, given the fact that you have 4 trials types, I don't > think this will be a large concern, especially if you are assuming an > hemodynamic response. > > If you can't get stable estimates of the 4 trials types, then the > subtractions will also be unstable. > ****I've always thought that unjittered, rapid event-related designs permit contrasts between trial types (i.e., subtractions) even though stable estimates of individual trial types are not possible. Are you saying this is not the case?*** > > One way to insert jitter into this design is to insert miniblocks --> > ABABABABAB pause BABBABABAB pause. The pauses help insert the jitter. > Another alternative is to use an ITI that is not matched to the TR. This > way each trial starts at a different point in the TR leading to sampling > the response at different timepoints throughout the response. > ****In the mini-blocks above, you alternate between trial types A and B. Is this just a coincidence or are you assuming an alternating design? I was actually thinking about a first-order counterbalanced design..... > > >> >> If so, then, at the nuts-and-bolts level, would I model the 4 sequential >> trial types above (i.e., A preceded by A, A preceded by B, B preceded by A, >> B preceded by B) and then form contrasts based on the resulting betas? If >> so, then would the A - B contrast be formed by contrasting >> >> AVERAGE (A preceded by A, A preceded by B) >> >> MINUS >> >> AVERAGE (B preceded by A, B preceded by B)? >> > > > Yes. This is how to form the contrasts. > ***Thanks! > > >> >> >> 2. To be able to look at the common effect of all stimuli vs. baseline >> (e.g., to functionally define ROIs for subsequent orthogonal contrasts, >> I've been considering breaking up the run into alternating periods of task >> and rest as suggested by Rik Henson (2006, Chapter 15 from Human Brain >> Function, I think). As Rik states on page 209 of this chapter: >> >> "Another problem with null events is that, if they are too >> rare (e.g. less than approximately 33 per cent), they actually >> become ‘true’ events in the sense that subjects may be >> expecting an event at the next SOA and so be surprised >> when it does not occur (the so-called ‘missing stimulus’ >> effect that is well-known in event-related potential (ERP) >> research). One solution is to replace randomly intermixed >> null events with periods of baseline between runs of >> events (i.e. ‘block’ the baseline periods). This will increase >> the efficiency for detecting the common effect versus >> baseline, at a slight cost in efficiency for detecting differences >> between the randomized event-types within each >> block." >> >> In this chapter, Rik doesn't refer to previous event-related studies that >> have employed this approach. Do you know of any? >> > > I am not aware of studies, but this is exactly what I suggested above. You > will lose a few trials that don't have any preceding trial, but that > shouldn't have a huge impact. > > ***Ok - sounds good! > >> Also, if I employed Rik's suggested approach, does the following sound >> reasonable? I'd consider breaking up the 96-trial-long sequence in each run >> into four 24-trial-long sequences and then separating each of these >> 24-trial-long sequences with a fixation block. Since trial duration is >> about 2 seconds, this would amount to alternating between 48-second-long >> task blocks and (I was considering) 20-second-long fixation blocks. While >> this is far from the most efficient block design (i.e., 16 seconds on, 16 >> seconds off), I'm thinking it may still allow me to observe the common >> effect of all stimuli vs. baseline as well as the contrasts between >> different sequential trial types discussed earlier. Does this sound right? >> > > I think that would be fine. Just remember to have fixation at the > beginning and end of the run as well. I also think you need more than 96 > trials. I would say you need at least 128 analyzable trials + the trials at > the beginning of each block. If you only want to use correct trials, then > you will need more than the 128 trials. 128 trials is 32*4. > > >> >> ****Ah, yes, I should have said 96 trials per run, not total. I plan to have several runs and so should get way more than 96 trials total. > 3) Adding one more level of complexity to the design above, I may also >> wish to model with different regressors different trials from a given trial >> type (e.g., A preceded by B) depending on the speed with which a subject >> responds. For example, I might want to model the 20% fastest trials >> separately from the 20% slowest trials within each trial type. In general, >> event-related fMRI allows for such "post-hoc" creation of trial types, but >> I just wanted to check whether anyone sees a problem with this procedure in >> the designs I am asking about. >> > > This isn't necessarily a problem; however, the post-hoc shorting will > leave you vastly underpowered. At 128 trials (or 32 per event type), 20% > would be 6-7 trials - which is not enough to be able to get a stable > estimate of the BOLD response. Also, depending on the task, you might want > to simply model RT as the duration of the trial. If you want to look at > response speed, then you would need 640 trials to get enough power. This > would be 32 trials in each trial type quintet. > *****When you say "if you want to look at response speed", do you mean "if you want to divide each of the four trial types into 5 quintiles based on RT?" If so, I understand why you recommend 640 trials. Out of curiosity, why do you recommend 32 trials for each "condition" or "trial type". What's special about 32? > > As an aside, using 0 second duration prohibits any PPI analysis on the > data. > > *** I was thinking that not jittering the ITI might complicate PPI analyses. Does including rest periods between "task blocks" as you recommend alleviate this complication? ***Also, why would modeling each trial with a duration of 0 seconds prohibit any PPI analysis on the data? And, what's the remedy? Model each trial with a constant non-zero value (e.g., 0.5 s) or with a duration equal to the RT? > >> Finally, if you've read this far, THANK YOU!!!!! >> > > > Hope you find this useful. > ***Very much so! Thanks!!!!! > > >> >> Best regards, >> Daniel >> >> -- >> Daniel Weissman, PhD >> Associate Professor >> Department of Psychology >> University of Michigan >> 1012 East Hall >> 530 Church Street >> Ann Arbor, MI 48109 >> > > -- Daniel Weissman, PhD Associate Professor Department of Psychology University of Michigan 1012 East Hall 530 Church Street Ann Arbor, MI 48109