### How does smoothing (on individual resting-state data) change the results?
After applying mean filtering of a gaussin kernel of 1.8mm sigma, residual predictions were greatly improved at dimension 25 ([figure1](figs/smooth25.png)) and dimension100 ([figure2](figs/smooth100.png))
* After applying mean filtering of a gaussin kernel of 1.8mm sigma, residual predictions were greatly improved at dimension 25 ([figure1](figs/smooth25.png)) and dimension100 ([figure2](figs/smooth100.png)) using ICA basis
* After smoothing, however, using coefficients averaged from 100 best-matched subjects only marginally improved prediction accuracy compared with using randomly selected subjects: [figure3](figs/best100.png)
