Table 5.

Results comparing 1). p-Eigen with Ward Clustering based ROIs and 2). p-Eigen with AAL ROIs vs p-Eigen with Ward Clustering based ROIs. 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. Note: The reported p-values are from a two sample t-test.

#	FeatureSet	MCI vs. Normal (μ ± σ)	Delayed Recall (All) (μ ± σ)	Delayed Recall (Patients) (μ ± σ)
1.	Ward Clustering	0.33 ± 0.08	2.24 ± 0.23	1.21 ± 0.24
2.	p-Eigen (Ward)	0.28 ± 0.05	2.07 ± 0.26	1.10 ± 0.22
3.	p-Eigen (AAL)	0.24 ± 0.06	1.83 ± 0.29	0.97 ± 0.18
	p-value	(2. vs 3.) 0.045 (1. vs 3.) 2.7 × 10−6	(2. vs 3.) 0.041 (1. vs 3.) 1.4 × 10−7	(2. vs. 3) 0.066 (1. vs. 3) 3.4 × 10−5