Hi,

On 15 Oct 2008, at 16:09, Ronan Joseph Kelly wrote:

> Dear FSL-users
>
> I am conducting a VBM analysis on a longitudinal study involving the  
> effect of different diets on brain volume. Participants were scanned  
> at 3 different time-points; T1, T2, T3. There ended up being 3  
> subgroups at each time-point; one group were control subjects with a  
> regular diet throughout the entire scanning period T1 - T3, the  
> second group was on a regular diet between T1 and T2 and on the  
> special diet between T2 and T3 (labelled 'Short term'), while the  
> third group was on the special diet between T1 and T3 (ie 'Long  
> term'). So in all, I have 9 (3 time-points by 3 diet levels)  
> different groups of interest.
>
> My questions involve using FSL-VBM on a longitudinal study of this  
> nature.
>
> My initial idea was to simply compare across diet level (ie Controls  
> VS 'Short term' special diet VS 'Long term' special diet etc).  
> However, I began to consider whether it was correct to include  
> multiple scans of participants at different time points in one  
> individual EV. If I was to pursue this approach I would have 3  
> different scans for each participant in each group. Is that allowed?  
> How should I carry this out?

The most flexible model is to have 9 EVs, one for each of the 9  
groupsXtimepoints. This would not have the correct degrees-of-freedom  
in a parametric analysis, but as you are using randomise it should be  
allowable I think. If you are looking for longitudinal-difference  
contrasts you can improve the estimation of longitudinal changes by  
then altering this design to match the 'paired' and 'tripled' examples  
as seen in the FEAT manual (under the details -> higher-level analysis  
examples). Alternatively you could just pre-subtract the data in order  
to feed in longitudinal changes directly, simplifying the model.

> My other idea was to divide all my data into the 9 different groups,  
> and to set up the contrasts in my Glm model using linear  
> combinations of these subgroups. For example, a contrast exploring  
> the effect of diet between controls and 'long term' particpants  
> would have to compare all 3 subgroups of the controls, (minus) all 3  
> subgroups of the 'long term' subjects. Again, is this the correct  
> method for carrying out such an analysis? Splitting up the subjects  
> into subgroups like this would enable me to carry out other  
> interesting contrasts using this linear combination method, but how  
> practical is it? And once I reach the stage of setting up my  
> contrasts, what coefficients would I use when for a simple contrast  
> I would simply use +1 and -1?

You need to decide whether you want to allow full flexibility in your  
modelling of different groups and different timepoints (as suggested  
above), or whether to make simplifying assumptions about different  
effects in different groups - you might want to try modelling things  
both ways to get a feel for how linear/additive the group memberships  
and timepoints are as factors.

Hope this helps, Cheers, Steve.


> I know that was long-winded but I'd appreciate any suggestions  
> people had to offer!
>
> Cheers,
> Ronan
>
> -- 
> Ronan Kelly
> Neuroinflammatory Research Group
> Trinity College Institute of Neuroscience
> University of Dublin, Trinity College
> Dublin 2
> Ireland


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