Hi,
being only a user (but not a developer) of MELODIC, I would suspect
that you will not be able to pick up a "reward" or "loss" component,
even if you specified correct *.con and *.mat files. In my experience
from a finger tapping paradigm, we were able to extract components
relating to "right hand" or "left hand" and perhaps something like
"cognitive control" among others. Dual regression on some of these
maps yielded interesting results (patients and controls were
involved).
In a Theory of Mind setting, however, we only saw Default Mode
Networks (probably relating to the null events) and something like
attention networks (plus motor stuff probably relating to the button
presses). No "first person" or "third person" perspective maps :-) It
is my impression that for this kind of questions - particularly if you
would model it as an event-related design - a GLM approach is still
the best. MELODIC will complement that by adding information (or
improve data if you use it for quality control and artefact removal).
What you could try to do is either a) concatenate all time series
design matrices and feed them into MELODIC in the right order, or b)
split the ICA timecourses into the single-subject chunks and correlate
them with your regressor(s). I'm not sure if this is valid, however.
Maybe the pros can elaborate on that?
Best regards,
Cornelius
On Thu, Jul 1, 2010 at 6:20 PM, Michael Hallquist
<[log in to unmask]> wrote:
> Hi Christian,
>
> Thanks for the suggestion to use temporal concatenation -- it makes sense
> given the structure of the data. I suppose my confusion is how to specify
> the proper timeseries and subject/session design matrices for melodic. More
> specifically, because there are 4 sessions for each subject and each session
> has a different time series design matrix (although the same stimulus
> types), a single time series file doesn't describe the runs. In traditional
> event-related analyses (e.g., using FEAT), I would tend to follow the
> multi-session, multi-subject approach where lower-level stats are estimated
> for each run and are then combined per subject at the second level. I'm not
> sure how to achieve this sort of thing using the temporal concatenation ICA
> approach. More specifically, I want to know about how extracted ICA
> components relate to particular stimulus types (e.g., reward cue, loss cue
> in a gambling task) in the paradigm, but this requires a time series design
> matrix.
>
> What would you suggest to resolve this issue?
>
> Thanks very much,
> Michael
>
> On Tue, Jun 29, 2010 at 5:08 PM, Christian F. Beckmann
> <[log in to unmask]> wrote:
>>
>> Hi
>>
>> If the timecourses associated with any effect are not identical between
>> subects/sessions then TICA is not appropriate. You can use the 'temporal
>> concatenation' approach instead
>> hth
>> Christian
>>
>>
>>
>> On 28 Jun 2010, at 21:07, Michael Hallquist wrote:
>>
>> > Dear FSL users,
>> >
>> > I am learning melodic in order to undertake an ICA of an event-related
>> > task using an existing dataset. Although I have read several papers on this
>> > topic and have explored the melodic practical from the FSL course, I am
>> > uncertain whether Tensor ICA or Temporal Concatenation ICA is appropriate
>> > for my data.
>> >
>> > I have approximately 30 subjects, 15 adults and 15 adolescents who
>> > completed four runs of the same task. The task was pseudorandomized across
>> > runs such that all participants saw exactly the same four runs, but the
>> > order of stimulus presentation differed per run. Because all participants
>> > received the same stimulation, I had hoped to use Tensor ICA. But because
>> > sessions and subjects are both higher-level effects, I didn't know whether
>> > this could be done. One alternative I had considered is temporally
>> > concatenating each participant's data and running a multi-subject,
>> > single-session (from the perspective of melodic) Tensor ICA.
>> >
>> > What would you advise?
>> >
>> > Thanks very much for your help,
>> > Michael
>
>
--
Dr. med. Cornelius J. Werner
Department of Neurology
RWTH Aachen University
Pauwelsstr. 30
52074 Aachen
Germany
Institute of Neuroscience and Medicine
MR Physics - INM4
Research Centre Juelich
52425 Juelich
Germany
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