Commit 888a0f16 authored by Ying-Qiu Zheng's avatar Ying-Qiu Zheng
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### 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)) 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/ukb_best100.png)
* After applying mean filtering of a gaussin kernel of 1.8mm sigma, residual predictions were greatly improved at dimension 25 ([figure 1](figs/smooth25.png)) and dimension 100 ([figure 2](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: [figure 3](figs/ukb_best100.png)
### 1) Does weighted average have added advantage? How is the prediction using bases averaged from other subjects?
* Results on UKB data are shown in [figure 3](figs/ukb_best100.png). Left panel shows the prediction using bases of the new subject, the right panel using bases averaged from other subjects (either 100 best-matched/randomly-selected/least-matched); green and orange boxplots show predictions based on weighted and unweighted averaged of the 100 best-matched subjects respectively. Including weights has minor effect on prediction accuracy...
* This is also true on HCP data [figure 4](figs/hcp_best100.png)
### Remove demeaning of components in melodic
* On UKB data: fastica.m was run on the first 25 columns of MIGP (voxelsX25) with numIC=25 (seems there is no demeaning) and the corresponding DR maps were created for each subject, which improved the prediction accuracy [figure 5](figs/fastica25.png)
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