Table 6.

Results showing p-Eigen (AAL) with Graphical Lasso better than p-Eigen using Pearson’s correlation. For MCI vs. Normal we report mean classification error (e.g. 0.24 ↝ 24%), whereas for Delayed Recall we report Mean Absolute Prediction Error ( ${\sum }_{i=1}^n\frac{\Vert y_i-{\widehat{y}}_i\Vert }{n}$). All the classifiers also used Base Features in addition to the graph measurement features from ROIs. All other p-values for each column were 2.2 × 10⁻¹⁶.

#	FeatureSet	MCI vs. Normal (μ ± σ)	Delayed Recall (All) (μ ± σ)	Delayed Recall (Patients) (μ ± σ)
1.	AAL (Pearson)	0.39 ± 0.05	2.51 ± 0.23	1.57 ± 0.28
2.	PCA (Pearson)	0.38 ± 0.06	2.59 ± 0.37	1.46 ± 0.21
3.	p-Eigen (Pearson)	0.31 ± 0.04	2.09 ± 0.31	1.11 ± 0.11
4.	p-Eigen (GLasso)	0.24 ± 0.06	1.83 ± 0.29	0.97 ± 0.18
	p-value	(3. vs 4.) 1.3 × 10−4	(3. vs 4.) 2.1 × 10−3	(3. vs. 4) 3.3 × 10−4