Hi Steve,
Unfortunately, stimuli were administerd with a random jitter. So
tensor ICA would not work, I guess.
We would like to remain fully exploratory throughout the whole
analysis, so we would like to avoid a timeseries model with a priori
hypotheses about hrfs.
Instead we want to try to timelock neural responses of the events and
sort IC-timecourses, which are event related (showing an average
response (e.g. are not distributed around zero, in each timepoint)
from IC timecourses, which are not due to events (e.g. which shoul
distribute around zero, when averaged).
As mentioned before I tried this sorting procedure with single
subject PICA and it seemed to work out well, but I'm not really sure,
about a concatenated PICA. Do the columns in the t**.txt files
represent the IC-timecourse for each subject? Finally, do we get
many more ICs with 20 subjects than with 10? Or does it make sense to
constrain the analysis to a certain number of ICs?
Cheers!
Andreas
Am 13.03.2008 um 09:51 schrieb Steve Smith:
> Hi - if you have the same timeseries model for all subjects then
> the easiest thing to do is to use Tensor-ICA (not concatenation)
> and enter the timeseries model in the MELODIC GUI in the same way
> that you do for single-subjects PICA analysis.
>
> Cheers, Steve.
>
>
> On 11 Mar 2008, at 09:51, Andreas Pedroni wrote:
>
>> Hi
>>
>> We are currently working on a study, where we try to identify neural
>> structures responding to tooth pain. We already analyzed data with a
>> classical GLM. Now we try to run MELODIC to possibly find other
>> neural
>> responses to administerd tooth pain. Our idea is, to rund a group
>> PICA
>> (concatenated subjects). In a second step we try to identify pain
>> related
>> ICs by timelocking IC timecourses and testing for each IC
>> timecourse, if
>> the time locked timecourse follows a regular run (e.g. IC
>> timecourses which
>> are not due to the pain stimulus should distribute around zero,
>> when time
>> locked to events, like a ERP). I already tried this with single
>> subject PICA
>> of data of an event related design and my results seemed to make
>> sense,
>> regarding the timecourses and the structures found. Now, my
>> questions are:
>> Can we do the same thing with the concatenated version of PICA? Is
>> it true,
>> that in the t**.txt files each column represents the timecourse of a
>> subject? Finally, do we get many more ICs with 20 subjects than
>> with 10? Or
>> does it make sense to constrain the analysis to a certain number
>> of ICs?
>>
>> Thanks for any hints!
>>
>> Cheers
>>
>> Andreas Pedroni
>> Instiute of Psychology Zurich, Neuropsychology
>>
>>
>
>
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> Stephen M. Smith, Professor of Biomedical Engineering
> Associate Director, Oxford University FMRIB Centre
>
> FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK
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