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. Author manuscript; available in PMC: 2014 Nov 15.
Published in final edited form as: Neuroimage. 2013 Jun 5;0:87–100. doi: 10.1016/j.neuroimage.2013.05.118

Figure 4. Parameter tuning of RSMFC on the simulated dataset.

Figure 4

(a) Changes in Euclidean distance between zero and the vector of group-level voxel-wise t-statistics with respect to the subspace size. The optimal subspace size is selected as the percentage change in distance less than or equal to 10%, which is indicated by the dashed line. The optimal subspace size is chosen as 40 voxels for the simulated data. (b) Percentage change in distance with various subspace sizes. The dashed line marks the convergence criterion of 1%. RSMFC converges robustly within 200 partitions. RSMFC = random subspace method functional connectivity. GSReg = global signal regression method.