Table 5.
Principal Components Regression for Prediction of Learning and Memory, Processing Speed, and Executive Function Scores from Cortical Brain Volumesa
|
Overall model statistics with all cortical components as predictors | |||||
|---|---|---|---|---|---|
| Model # | Predicted | Model parameters | Sum of squares | Mean square | F-value (p value) |
| 1 | L&M | Regression | 9.70 | 3.23 | 6.07 (0.005)* |
| Residual | 9.06 | 0.53 | |||
| 2 | PS | Regression | 10.82 | 3.61 | 3.31 (0.041)* |
| Residual | 21.77 | 1.09 | |||
| 3 | Exec | Regression | 9.86 | 3.29 | 2.03 (0.142) |
| Residual | 32.41 | 1.62 | |||
|
Contribution of input variables to model strength | ||||
|---|---|---|---|---|
| |
|
Standardized beta coefficients (p value) |
||
| Input variable | Cortical regions | L&M | PS | Exec |
| Component 1 | Sup frontal and Parietal | 0.40 (0.032)* | 0.52 (0.010)* | 0.45 (0.033)* |
| Component 2 | Inf parietal | 0.28 (0.119) | 0.16 (0.403) | 0.10 (0.610) |
| Component 3 | Precuneus | 0.52 (0.006)* | 0.20 (0.289) | 0.15 (0.461) |
*
Significant at p < 0.05.
a
Principal components regression was conducted using three components derived from cortical brain volumes to predict each of the three composite cognitive measures (top rows), and to determine which cortical components contributed the most to neurocognitive outcome (bottom rows).
L&M, learning and memory; PS, processing speed; Exec, executive function; Sup, superior; Inf, inferior.