Dear Alfonso and other CONN users,

I was hoping you might be able to enlighten me regarding the appropriate use of Network Based Statistics in CONN.

I am investigating a network of 40 seeds in 20 individuals and I want to determine if there are changes in intranetwork connectivity that correlate with a continuous behavioural measure. I am most interested in determining whether the network as a whole varies with the behavioural measure, but would also be interested in identifying particular nodes that are most implicated

My questions are:

1. I select all 40 seeds. I threshold ROI-ROI connections by p<0.05 uncorrected. I then threshold SEED ROIs (NBS; by size) by p<0.05 FDR. For the seeds that are found to be significant here, is it appropriate to say that their connectivity to the network significantly correlates with the task performance?

2. I threshold ROI-ROI connections by p<0.05 uncorrected. I then threshold NETWORKS (NBS; by size) by p<0.05 FDR. My results are: 1 significant network with 17 positively correlated edges and 1 negatively correlated edge. What I want to test is whether increased connectivity within the network correlates with the task performance, but it seems that as the graphs can contain negative edges this may not really be testing that?

3. In the examples above the initial ROI-ROI threshold can have a significant impact on results, is there a danger of increasing false positives by using this relatively relaxed threshold (as e.g. occurs with conventional cluster based statistics in the Eklund PNAS paper). It seems to me that we don't have to worry about this due to the NBS permutation testing approach?

4.My understanding is that NBS is testing for the size of graph components. Unless we have shown that there are no differences in connectivity strengths, however, is it possible to say whether a difference as tested by NBS is better understood as representing a change in connectivity strengths or a difference in graph structure?

Many thanks and please let me know if the questions are unclear,

Rob