Table 4.
Principal Components Regression for Prediction of Learning and Memory, Processing Speed, and Executive Function Scores from Subcortical Brain Volumesa
|
Overall model statistics with all subcortical components as predictors | |||||
|---|---|---|---|---|---|
| Model # | Predicted | Model parameters | Sum of squares | Mean square | F-value (p value) |
| 1 | L&M | Regression | 9.55 | 3.18 | 5.88 (0.006)* |
| Residual | 9.20 | 0.54 | |||
| 2 | PS | Regression | 14.67 | 4.89 | 5.46 (0.007)* |
| Residual | 17.91 | 0.90 | |||
| 3 | Exec | Regression | 11.96 | 3.99 | 2.63 (0.078) |
| Residual | 30.32 | 1.52 | |||
|
Contribution of input variables to model strength | ||||
|---|---|---|---|---|
| |
|
Standardized beta coefficients (p value) |
||
| Input variable | Subcortical regions | L&M | PS | Exec |
| Component 1 | Amygdala, hippocampus | 0.38 (0.040)* | 0.23 (0.175) | 0.24 (0.217) |
| Component 2 | Thalamus | 0.38 (0.041)* | 0.63 (0.001)* | 0.38 (0.060) |
| Component 3 | Corpus callosum | 0.50 (0.009)* | 0.08 (0.658) | 0.29 (0.145) |
*
Significant at p < 0.05.
a
Principal components regression was conducted using three components derived from subcortical brain volumes to predict each of the three composite cognitive measures (top rows), and to determine which subcortical components contributed the most to neurocognitive outcome (bottom rows).
L&M, learning and memory; PS, processing speed; Exec, executive function.