A large amount of data is now available about the pattern of connections between brain regions.
Computational methods are increasingly relevant for uncovering structure in such datasets.
Normally, it is applied to data matrices containing a large number of levels of dissimilarity, whereas for connectivity data there is a very small number. We address the suitability of NMDS for this case. In terms of the framework presented above, F is a dissimilarity matrix. An evaluation of the use of Multidimensional Scaling for understanding brain connectivity. An evaluation of the use of Multidimensional Scaling for understanding brain connectivity.