Commit a4464c1e authored by Ying-Qiu Zheng's avatar Ying-Qiu Zheng
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......@@ -25,7 +25,8 @@ All the above results are based on using residual bases to predict residual task
* We saw that using residual bases to predict task activation further improves model accuracy ([Figure 9](figs/ukb_addback.png): UKB data; [Figure 10](figs/hcp_addback.png): HCP data). Yellow boxes: correlation between actual and predicted task activations (using residual bases) with group-level effects added back; Blue boxes: correlation between actual and predicted task activations (using original bases).
* To quantify how the diagonals of the correlation matrices differentiates from the off diagonal elements, we calculated the Kolmogorov–Smirnov test statistic as a measure of distance between the distributions of diagonal elements and off-diagonal elements (for a given sample size this statistic provides a comparable distance metric). We found using residual bases to make predictions further enhances the diagonal correlations ([Figure 11](figs/ukb_diag.png): UKB data; [Figure 12](figs/hcp_diag.png)), suggesting it has added advantage in capturing the individual variability in how subjects respond to tasks.
### 4. Prediction of amplitude of group-level activation maps
We also investigated whether amplitudes of group activation maps, which are the effect size (betas) of group-level contrast maps in explaning individual task activation maps, can be predicted by the amplitude of bases, which are the standard derivations of the individual time courses (in dual regression), across subjects. 80% of the subjects were taken as training data, and the rest 20% were used to evaluate the prediction. The process was repeated 1000 times for both UKB ([Figure 13](figs/ukb_amplitude_prediction.png)) and HCP ([Figure 14](figs/hcp_amplitude_prediction.png)) dataset.
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