Commit d4a9167f authored by Ying-Qiu Zheng's avatar Ying-Qiu Zheng
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### 1. Comparison of bases in reconstructing task activation maps
#### We first evaluated the effect of dimensions (i.e., number of bases) on model accuracy of reconstructing task activation maps (i.e., Pearson's correlation between reconstructed task activation maps and the actual activation maps). For each method (PCA, ICA, or Laplacian Eigenmaps), the individual bases were derived by running dual regression at a specific dimension each time for each subject. On HCP data, we compared the bases at dimensions 15, 25, 50, and 100; on UKB data, we compared 25, 100, and 200. The set of bases were then regressed against individual task activation maps to get reconstruction coefficients for each subject. To predict task activation maps of a new subject, we used the subject's own bases and the averaged reconstruction coefficients of 100 unseen subjects.
We first evaluated the effect of dimensions (i.e., number of bases) on model accuracy of reconstructing task activation maps (i.e., Pearson's correlation between reconstructed task activation maps and the actual activation maps). For each method (PCA, ICA, or Laplacian Eigenmaps), the individual bases were derived by running dual regression at a specific dimension each time for each subject. On HCP data, we compared the bases at dimensions 15, 25, 50, and 100; on UKB data, we compared 25, 100, and 200. The set of bases were then regressed against individual task activation maps to get reconstruction coefficients for each subject. To predict task activation maps of a new subject, we used the subject's own bases and the averaged reconstruction coefficients of 100 unseen subjects.
* [Figure 1](figs/hcp_bases_comparison.png). shows the boxplots of model accuracy (correlation) across 100 HCP subjects at four different dimensions (from left to right panel, 15, 25, 50, 100) for each set of bases respectively (ICA: green; PCA: orange; Laplacian Eigenmap: blue). Overall the choice of bases and dimensions has minor effects on model accuracy in predicting task activation maps.
* [Figure 1](figs/hcp_bases_comparison.png). shows the boxplots of model accuracy (correlation) across 100 HCP subjects at four different dimensions (from left to right panel, 15, 25, 50, 100) for each set of bases respectively (ICA: green; PCA: orange; Laplacian Eigenmap: blue). Overall the choice of bases and dimensions has minor effects on model accuracy in predicting task activation maps.
* [Figure 2](figs/ukb_bases_comparison.png). shows the model accuracy in predicting task activation on 100 UKB subjects at dimension 25 (left panel), 100 (middle) and 200 (right panel) for ICA (green), PCA (orange) and Laplacian Eigenmaps (blue). Increases in bases dimension tend to decrease model accuracy possibly due to overfitting. Meanwhile, ICA dual regression maps outperforms the other two approaches in reconstructing the three contrast maps. It is also interesting to note that the model accuracy boxplots across subjects has smaller quartile range at higher dimensions (which is also true on HCP data, although not as obvious).
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