Hi

On 19 Nov 2007, at 22:38, Christopher Bell wrote:

> I have run a group analysis on RSN data and am trying to correctly
> interpret the output. I have specified 4 contrasts.

I suspect you mean across the subject domain? This does not make that  
much sense - the concat approach differs from full TICA in that the  
rank-1 aroximation is not part of the estimation. It is, however,  
being run post-hoc (after the components have been estimated free- 
form) because in some cases one might be interested in not using the  
approximation during the estimation but still might be interested to  
see how well the largest Eigen- time course represents variation  
across subjects.

> How are the subject mode
> effect sizes calculated?

These correspond to the factor loadings after taking all different  
time courses, assembling them into a matrix and calculating it's  
single largest EIgenvector.


> Is their a technical report that discusses this?

This rank-approximation is described in the T-ICA paper, see the  
technical report at http://www.fmrib.ox.ac.uk/analysis/techrep/tr04cb1/tr04cb1.pdf

> I
> am mainly interested in determining what is the cause of specific  
> subject
> being an outlier for a component. I.e. is it a difference in this  
> specific
> subject from the averaged spatial map or from the averaged time- 
> course?


>
>     Also, I believe the subject mode effect size has been referred  
> to as a
> vector of spatial-temporal subject specific differences.

Yes, in the context of tica

> This slightly
> confuses me since with multi-session temporal concatenation mode, I  
> thought
> the time-course was assumed to be variable between subjects (unlike  
> with
> tensor ICA).

that's right

> I don't understand the utility of an "averaged time-course" or
> the contribution of a timecourse to a subject's effect size, given  
> that the
> subject's timecourse is not assumed to be consistent with other  
> subjects'
> timecourses during the decomposition.

You still might be interested in checking how consistent the effect is  
in it's temporal characteristics across subjects, e.g. assume that you  
do a learning task where (because people differn in the way they  
learn) you do not want to use full tica to constrain the time course  
to be the same. You therefore choose to estimate the components using  
the concat apraoch but might still be interested to see how well the  
'average' time course (rank-1 in fact, which is different) does  
capture the full set of variation in subjects' learning.

>
>   More broadly, if the t-tests turn up a difference between groups  
> is there
> information about where this difference is spatially?

In all cases (concat or tica) the estimated difference always is  
related to the entire spatial map associated with the time courses

> Are these differences
> calculated similarly to randomise?

No, the report says that it's a simple GLm using ordinary leasts  
squares.

> Is it possible to show the location of
> between group differences in melodic

It's related to the entire spatial map.
hth
christian

> or is it best to run the groups
> separatly and then compare the z-maps to find spatial-specific  
> differences
> between groups? Sorry for so many questions!
>
> Chris Bell