Commit b799458e authored by Ying-Qiu Zheng's avatar Ying-Qiu Zheng
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......@@ -15,10 +15,10 @@ We next matched the bases of each subject to the rest in the pool (HCP: 967 subj
To investigate why there are less improvement in using best matched subjects on HCP data, for each subject we averaged its ``matchness'' with all other subjects to get a scalar value, which roughly measures how much the subject's bases differs from the others. We plotted the distribution of this measure for each dataset ([Figure 5](figs/ukb_matchness.png): UKB data; [Figure 6](figs/hcp_matchness.png), HCP data). The relative range of matchness on UKB dataset is much higher than HCP dataset (far right side matchness is almost seven times larger than the left side on UKB), providing a possible explanation why more improvement can be achieved on UKB dataset by selecting better-matched subjects.
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Correlation maps between actual and predicted activation were constructed for each dataset ([Figure 7](figs/ukb_corr_mat.png): UKB data; [Figure 8](figs/HCP_corr_mat.png) for HCP data). The diagonals of the matrices show higher correlation for all tasks, indicating that the reconstructed maps more closely matched the same subject compared to other subjects.
### 3. Reconstructed task activation maps versus actual task activation maps
Correlation maps between actual and predicted activation were constructed for each dataset ([Figure 7](figs/ukb_corr_mat.png): UKB data; [Figure 8](figs/hcp_corr_mat.png) for HCP data). The diagonals of the matrices show higher correlation for all tasks, indicating that the reconstructed maps more closely matched the same subject compared to other subjects.
### 3. Comparison of prediction using residual bases and original bases
When
All the above results are based on using residual bases to predict residual task activation maps. To investigate how it improves model accuracy compared to using original bases (to predict original task activation maps), we added the effect of group-level task activation maps back to the residual maps (both residual and predicted) so that the two approaches are comparable. 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).
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