Howdy

To give an overview of what I have done so far, I have estimated individual seed-to-target connectivity (2 seeds, 8 ROIs) values for subjects across a 4-wave longitudinal study. My question is: how can I estimate networks from these ROIs? The main question I am trying to answer is: does the connectivity with these seeds and ROIs change as a function of development?

I used FSL's dual regression to estimate the seed-to-target connectivity values, but, I am looking at 3LME as a potential option to identify "clusters" of interest (seems preferable to PALM/ randomise given how it models missingness). Likewise, looked into FSLnets, but am unclear on how to use that technique longitudinally. Regardless, after examining the patterns of connectivity, the pattern seems largely consistent with a priori hypotheses (i.e., "reward" seed connects to "reward" ROIs, "executive" seed connects with "executive" ROIs). In order to test whether the pattern of connectivity varies as a function of the seed region however is the part where I am getting stuck. Is there a way to model whether particular target ROIs form a network with a given seed? In essence, I'm trying to validate the seed-to-target connectivity patterns and see if these "networks" vary across development. I've seen people who run 3dLME and then correct for fwe and cluststim, but have not yet found any method that gets at my main question: do seed-to-target (across target ROIs) connectivities: 1) form distinct networks; 2) do these networks vary as a function of development? If I were to use 3dLME, is there a way to specify the target regions in that analysis in order to identify distinct clusters?

I realize too that this might be a circular analysis, in which case I would likely just fall back on whole brain analyses. The longitudinal nature of the analysis complicates things tremendously. Any guidance or advice would be appreciated! I am a (relative) newcomer to rsMRI analyses, so please let me know if anything is unclear. I am open to alternative approaches (obviously).

-Sam