What are different ways to determine centroids of fMRI activation, their drawbacks and perks? I'm reading an older article on bilingualism (Kim, Relkin, Lee, &amp; Hirsch, 1997) for a seminar. They were interested in the spatial separation of two languages in early and late bilinguals.  They determined activated regions and their overlap in two conditions and across two groups.

To determine these areas, they first excluded all voxels where BOLD response difference to baseline was not above their empirically derived false positive rate (p&lt;0.0005) and then used all these voxels as if they had the same activation to determine the "centre-of-mass".

They then calculated the distance between the centroids in the two conditions to measure spatial separation and counted the overlapping voxels.

I have some issues with this method:


it seems as if they're throwing away a lot of information by discarding all voxels where activity was not above chance level and maybe more importantly by treating all significantly activated voxels as the same even though there may be significant differences in their activation level 
minor: it's volumetric, not mass, so it should be centre of volume, right? It may be a misunderstanding of the workings of fMRI on my part.


They also demonstrated that the derived distance held up well over two orders of magnitude in significance level, but still they didn't differentiate between highly and not-so-highly-but-still-significantly activated voxels. Is this still state-of-the-art?

My questions:


What are different ways to calculate centroids without discarding so much information but still backing up the conclusion inferentially (some sort of spatial confidence area?)?  
What are their drawbacks (my lecturer says they all have some, so you can just as well use the above-mentioned multistage method, but this seems like the easy way out)?
What is a well-tested method to test spatial separation of activity for two conditions? I'm not asking about demonstrating functional separation, but once you don't treat voxels as on-and-off anymore, calculating degree of overlap becomes less trivial.


References


Kim, K.H.S. and Relkin, N.R. and Lee, K.M. and Hirsch, J. (1997). Distinct cortical areas associated with native and second languages, Nature. FREE PDF
This question gets close to something that might alternatively be posted to stats.stackoverflow.com. Personally, I've always felt that application of Null-Hypothesis Significance Testing (NHST) methods to neuroimaging data does a particularly good job of highlighting their scientific deficiencies. 

Personally, these days I'd use Generalized Additive Modelling (GAM) to analyze data from an individual's scan and if aggregating multiple spatially normalized scans I'd use Generalized Additive Mixed effects Modelling (GAMM). In both approaches, I'd use an AIC-adjusted likelihood ratio to evaluate the strength of evidence for differences amongst groups/conditions in the activity patterns discerned by GAM. A posteriori investigation of any evidence-supported differences can be achieved by visualization and parametric bootstrapping of confidence intervals.

If you really want to explicitly evaluate evidence for a group difference in the distance between activation areas, then one approach is to assume an a priori model for the activation, say a mixture of two trivariate gaussians with a parameter that specifies their separation, then use something like hierarchical Bayesian modelling to establish credible intervals on the difference between groups on the separation parameter.