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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 8. Parameter tuning for RSMFC on the experimental rsfMRI dataset.

Figure 8

(a) Change 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%, as indicated by the dashed line. The optimal subspace size is chosen as 40 voxels for the experimental rs-fMRI dataset. (b) Percentage change in distance with respect to the number partitions for various subspace sizes. The dashed line indicates the convergence criterion of 1%. RSMFC robustly converges within 200 partitions. RSMFC = random subspace method functional connectivity. GSReg = global signal regression method.