Fig. 3.

A: Unconstrained MDS solution for the timeseries extracted from the Yeo et al. (2011) parcellation for the four networks of interest for a single subject. In this configuration, the timeseries data clearly separate out into the networks of interest. B: The constrained MDS solution for the timeseries extracted from the Yeo et al. (2011) parcellation for the four networks of interest for a single subject. Though the stress value for the constrained MDS solution increases, the stress score is not highly impacted when the network constraints are added in the constrained MDS, as the network groupings were already present in the unconstrained solution. C: The unconstrained MDS solution for the timeseries extracted from the NeuroSynth scheme for the four networks of interest for a single subject. In this configuration the timeseries data do not clearly separate out into the networks of interest. D: The constrained MDS solution for the timeseries extracted from the NeuroSynth scheme for the four networks of interest for a single subject. Even within the constrained MDS solution, the NeuroSynth data fail to clearly separate, which is reflected in the large stress score. The large difference in stress scores between the unconstrained and constrained MDS solutions for the NeuroSynth scheme suggests that the networks of interest are poorly represented in the extracted timeseries data for this subject.