If I've understood correctly, you've entered 1,2,3 for the behaviour variable - this shouldn't have worked, as all subjects in group 1 would have a value of 1, and the correlation could not be calculated..

can you provide some additional details??

nancy

Hi both

Well yes, I probably haven't done it correctly at all :)

So I assumed that I would enter group (1, 2, or 3) for each of the filename entries in one column for condition1, condition2 and condition3 for all 34 subjects (I don't have even numbers in each group unfortunately) so it would be

subj1 condi1 group1 

subj2 cond1 group2

subj3 cond1 group3

.

subj1 cond3 group1 

subj2 cond3 group2

subj3 cond3 group3 etc

Doing this I got a highly significant LV1 showing me a network that I would expect given the pathology of the 3 groups and a non significant LV2 

but I have no idea if that is the correct thing to do! I wanted to find brain LV's that would differentiate the groups in terms of the weightings of the different image types e.g in one network it loaded more on the Hurst maps and PET than on the GM maps in Group2 or it loads equally on both Hurst and GM in Group3. 

NB if I choose Mean centered PLS then it does give me a duplicate permutation error message :)

hmm  - I have a thought!

Treat this as a single group with each image as a 'condition' then do a behavior PLS with the grouping contrasts as the behaviors.  You probably want to use orthogonal coding:

v1: 2 -1 -1

v2: 0 1 -1

then what you should get for each LV is the weights for condition (images) will give you the image weights that differentiate the groups

It might work!

Randy

Hi

I finally got around to running this design.  Just to check; I had 2 conditions Grey Matter maps (GM) and Hurst Maps (HM), then I put in 2 columns of behavioural variables in the form you suggested 2 -1 -1 equating to NC - (D1+D2) and 0 1 -1 equating to D1-D2. Where D=disease.There were 34 subjects in total so 68 rows the first 34 being condition 1 and the second 34 being condition 2.  1000 permutations and 100 bootstraps. Output 4 LV pairs

Is it possible to upload a pdf attachment? Anyway my interpretation would be looking at each image LV and each Behav LV:

LV1 (p=0.0001) brain scores separates NC from D1and D2 in condition 1 (GM) r=-0.72 

LV2 (p=0.159) brain scores separates D1 from D2 in condition 1 (GM) r=-0.63 

LV3 (p=0.352) brain scores separates D1 and D2 in condition 1 (GM) r=0.71 and NC from D1and D2 in condition 2 (HM) r=-0.57

LV4 (p=0.734) brain scores separates D1 and D2 in conditon 2 (HM) r=-0.58.

For the Behavioural LVs (this bit I don't understand so much)

LV1 behav scores separates NC from D1 and D2 in condition 1 (GM) r=-0.89 

LV2 behav scores separates D1 from D2 in condition 1 (GM) r=-0.71 and NC from D1 and D2 in condition 2 (HM) r=-0.62

LV3 behav scores separates D1 and D2 in condition 1 (GM) r=0.55 and NC from D1 and D2 in condition 2 (HM) r=-0.73.

LV4  behav scores separates D1 and D2 in conditon 2 (HM) r=-0.94.