bcipolli
commented
7 years ago Hmmm.. does this try to answer the same question we've been looking at? For me, to do the above, I would think about running resting state analysis on HCP data across the whole brain vs. individual hemispheres. In other words, replicate the analysis done in the HCP papers, but compare half vs. whole brain, and characterize the degree of lateralization between networks.
In that analysis, we could look at functional connectivity between each half-brain network, to look at interhemispheric interactions after the unilateral networks were determined, and see if this (a) explains the variance of the data with fewer components (i.e. a better fit) and (b) shows different network laterality.
You say summarizing the distinct laterality profiles of different brain regions; that sounds like things other papers have done, right?
I feel I must be misunderstanding (and rambling)...
Interactive plots are nice, but I think we need some way of summarizing the distinct laterality profiles of different brain regions. i.e. Which brain regions/networks consistently show evidence for symmetric/asymmetric functional organization? What are the lateralized functions and lateralized networks of the brain?
One way I can think of doing this, and there may be other ways, is to get best-match score map (and possibly other measures... I have to think a bit), by averaging the n maps, each a product of best-match score and component map for each ICA component. Then use existing functional network atlas (Yeo, Power?) and get a summary of matching performance per network....does it make any sense?
If you can think of a better way of summarizing things so we can infer something about specific brain networks from our analyses, do let me know @bcipolli !