Commit b43d3217 authored by Ying-Qiu Zheng's avatar Ying-Qiu Zheng
Browse files

add new notes

parent 888a0f16
2020APR28/figs/ukb_idp.png

263 KB | W: | H:

2020APR28/figs/ukb_idp.png

421 KB | W: | H:

2020APR28/figs/ukb_idp.png
2020APR28/figs/ukb_idp.png
2020APR28/figs/ukb_idp.png
2020APR28/figs/ukb_idp.png
  • 2-up
  • Swipe
  • Onion skin
### 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 ([figure 1](figs/smooth25.png)) and dimension 100 ([figure 2](figs/smooth100.png)) using ICA basis
* 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. (Here the prediction was made using the first 100 subjects, leave-one-out) - and subsequently updated on ~12,000 subjects.
* 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)
* UKB subjects have larger range of ''matchness'' than HCP subjects [figure 5](figs/matchness_dist.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)
* 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 6](figs/fastica25.png). (Here the prediction was made using the first 100 subjects, leave-one-out)
* On HCP data: no improvements.
### Correlation of predicted amplitude with nIDP on UKB, ~12,000 subjects
* Left panel shows the correlation matrix between the selected four IDP (faces-shape related) and the nIDPs (age wearing glasses); middle panel the correlation matrix between amplitudes of the three task (faces, shape, faces-shapes) and the selected nIDPs (age wearing glasses); right panel the correlation matrix between predicted amplitude of the three tasks and the four nIDPs [figure 7](figs/ukb_idps.png); Neither task amplitudes nor predicted amplitudes shows is correlated with these nIDPs... (but the IDPs and the amplitudes are correlated though).
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment