Dear Doug,
Thanks for your reply. I will compare conditions against the NULL, but the ultimate aim is to compare conditions against each other. The effect I am looking for will be very small, so I am trying to make sure I optimise the design- I really appreciate your insight.
Miezin et al., 2000 NeuroImage showed that presenting flashing chequer boards with short ITI (min=2,5, mean=5s, TR=2.5 for events of 1.5 duration) resulted in highest power (mean z-score) for responses in the visual cortex compared to longer ITI. This is mainly due to the greater amount of trials shown per condition. They only used one stimulus condition and compared it to the NULL (consistent with scenario described in your point 2), however my aim is to compare different conditions against each other.
I am concerned that the reduction in amplitude of closely spaced trials will affect the between-condition comparison. If the second of two closely spaced trials will have a reduced amplitude (your point 1) - and it is the amplitude between conditions I am interested, I'd be wiping out my effect of interest.
If this is the case, introducing ITIs between each trial to bring back the amplitude of the second (and subsequent trials) would be better for between condition comparisons.
Does this sound right?
Many thanks for your patience in explaining.
Betina
On 04/24/2012 07:34 AM, Betina Ip wrote:
Dear Doug,
Thank you for your excellent answers, they are very helpful. Please allow me to summarise and ask for additional clarification:
- The main problem I'd like to solve is, as you spotted, whether the
sequence will have required power to detect and effect (if there is one!). So, please disagree with me if this is incorrect, but one way to evaluate the potential of the sequence in terms of detecting an effect would be to plug the condition files directly into a Fsl FEAT analysis design matrix (FEAT>Stats>Full Model Setup> Efficiency), which would come out with an efficiency/estimability % to detect a statistically significant effect for a given comparison. The lower the effect required, the more efficient the design.
yes, that's fine. You'll have to enter a guess as to the level of the noise and the size of the effect.
- The second question is related to spacing of NULL trials. I would
like to first run a univariate analysis and then run a multivariate analysis. You mentioned NULL trials would be 'helpful'- could you please clarify whether you mean having ANY null trials or having null trials between EACH trial would be helpful?
There are two answers to this. (1) The point I was trying make below is that two closely space non-null trials will result in a smaller amplitude for the 2nd. Putting a null between them helps to bring back the amplitude of the 2nd. For this you would need nulls between each event. (2) If you have a hypothesis in which you compare a condition against the null, then you need about as many null trials as you have any other condition and you don't necessarily need nulls between each condition (sounds like this is already the case).
My current design is allocating as much total null time as for any other condition (as recommended in freesurver list). This means that I do have some NULL trials, and they are randomly inserted in the sequence. However, most trials have no ITI between them. If I did force ITIs between EACH trial I would have to decrease number of trials for each condition type, which could lower the design efficiency.
IF the problem of having carry-over effects of closely very spaced trials (i.e. majority of trials with no separation between trials) could be alleviated by rigorous first order counter-balancing within Optseq2 using the --focb option, is it necessary at all to add a null trial between EACH trial into the design?
The carry over is not the problem, it is the reduction in amplitude for the 2nd of closely spaced trials.
doug
Thanks for your clarification.
Best,
Betina
Hi Betina, see answers below doug
On 04/23/2012 12:04 PM, Betina Ip wrote:
/ Dear Freesurfer list,
/>/ />/ I am using optseq2 for the first time and wanted to check with you whether my protocol makes sense, many thanks in advance for your insight. />/ />/ 1) I start by choosing the maximum scan duration equalling ~30 min: /You might want to break this up into shorter chunks. It's totally up to you, you just don't need to have everything in a single run.
/ 2) set the psd window so it can capture the entire waveform of a 4 second event
/>/ 3) select equal amounts of trials for each of the 4 event types, I played around with it and 80 trials give me the greatest efficiency (2.72) and variance reduction factor average (54.37). Are these values acceptable? They are the best I could get tweaking the number of trials. /You can't tell whether a VRF of 54 is good enough. You are asking a question about the power of your experiment, which optseq can't answer. If you don't have an effect, then no number of trials or efficiency will be good enough. Having said that, I usually shoot for a VRF of between 20 and 40, but that's just a rule of thumb.
/
/>/ The command is here: />/ />/ ./optseq2 --ntp 440 --tr 4 --psdwin 0 20 \ />/ --ev evt1 4 80 \ />/ --ev evt2 4 80 \ />/ --ev evt3 4 80 \ />/ --ev evt4 4 80 \ />/ --nkeep 3 \ />/ --o gfatrial \ />/ --focb 10 \ />/ --nsearch 10000 />/ />/ A sample trial list is here: />/ />/ 0.0000 2 4.000 1.0000 evt2 />/ 4.0000 4 4.000 1.0000 evt4 />/ 8.0000 0 4.000 1.0000 NULL />/ 12.0000 3 4.000 1.0000 evt3 />/ 16.0000 3 4.000 1.0000 evt3 />/ 20.0000 2 4.000 1.0000 evt2 />/ … />/ />/ The other question I had concerns the position of null trials. Many trials are not separated by a NULL event, however studies of rapid event-related designs to report a minimum ITI, in this case the minimum seems to be 0? To assess the effect of forcing a minimum ITI on the sequence, I have run the search defining the --tnullmin to 4 sec (equal to 1 TR) however efficiency and VRFAvg drop to .79 and 16.07, obviously due to trade of between ntp and ntrials (ntp=440, ntrials=62). />/ />/ Are NULL trials between each event required at all if a standard FIR is used for the analysis? My aim is to first apply a univariate BOLD-analysis and subsequently a multivariate analysis to the dataset. Thanks for clarifying. /They are not strictly necessary, however, they may be helpful. If you really don't want them as part of your design (eg, it would interfere with the psychology), then don't use them but make sure to optimize the FOCB more (I usually use 100 regardless). The disadvantage of not having the nulls is that there will probably be some non-linearity in that the second of two closely spaced trials will have a lower amplitude than if it occurred in isolation. This will hurt your power and might cause a false difference between conditions if they are not well counter-balanced. doug
/
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