Hi Diana,

You may compute the association between symptom scores and connectivity within each of your three subject groups using the following procedure:

1) create three second-level covariates (Group1, Group2, and Group3) indicating your three subject groups (e.g. Group1 contains 1's for the subject in the first group and 0's for everyone else)

2) create three second-level covariates indicating the symptom scores within each of your three groups (e.g. Scores1 will contain the symptom scores for subjects in Group1 and 0's for everyone else; if you already have a second-level covariate named 'scores' containing all of the subjects' scores you may enter in the 'values' field of the new 'Scores1' covariate "Group1.*scores" -without the quotes- to create this new 'Scores1' covariate)

3) in the second-level analyses enter any of the following:

  a) select 'AllSubjects' and 'scores' in the between-subject effects list and enter a between-subjects contrast [0 1] to look at the association between symptom scores and connectivity across all of your subjects (jointly across the three groups, disregarding group info)

  b) select 'Group1', 'Group2', 'Group3', 'scores', and enter a contrast [0 0 0 1] to look at the association between symptom scores and connectivity across all of your subjects (jointly across the three groups) after discounting potential differences in average connectivity between your groups

  c) select 'Group1' and 'Scores1' and enter a contrast [0 1] to look at the association between symptom scores and connectivity within your first group only

  d) select 'Group1', 'Group2', 'Scores1', 'Scores', and enter a contrast [0 0 -1 1] to look at the difference between Group1 and Group2 in their association between symptom scores and connectivity

  e) select 'Group1', 'Group2', 'Group3', 'Scores1', 'Scores2', 'Scores3', and enter a contrast [0 0 0 1 -1 0; 0 0 0 0 1 -1] to look at any differences between your groups in their association between symptom scores and connectivity
 
Regarding your second question, the beta values reported in the results table are the effect sizes of the chosen contrast. Mathematically if B is the matrix of regression coefficients of your second-level model, C is your between-subjects contrast, and D is your between-conditions contrast, then the effect sizes are C*B*D' (note: if any of your between-subjects or between-conditions contrast is a matrix instead of a vector then C*B*D' is a vector and the results table reports the norm of this vector). The interpretation of these betas depends on the chosen model and contrasts. For example, in the example (a) above, the effect size of the chosen contrast represents the association / regression-coefficient between symptom scores and functional connectivity, and its units are "increases in functional connectivity -fisher transformed correlation coefficients- associated with each unit increase in symptom scores". When looking at main connectivity effects (e.g. if you select 'AllSubjects' and enter a [1] contrast) then the reported effect sizes represent average functional connectivity -fisher transformed coefficients- among the tested subjects). Let me know if you would like me to further clarify any of the above.

Hope this helps
Alfonso


Originally posted by Diana P: