Table 5.

Comparison of the performance of our novel ThickNet features (shortened as TN in this table for lack of space) and mean thickness (MT) features based on the full cohort. We show that the improvement in AUC by ThickNet is significant relative to MT at p = 0.05 significance level in all the experiments, except for the classification between CN vs. sd-aMCI. p-Value for whether the performance of ThickNet features is significantly better than the combination of MT and hippocampal volumes, as well as a random classifier are indicated in the table.68·NPP

Experiment	AUC: MT	AUC: MT + HV	AUC: TN	p(TN > MT)	p(TN > MT + HV)	p(TN > Random)
NC vs sd-aMCI	0.60	0.58	0.61	> 0.05	> 0.05	< 0.01e− 5
NC vs md-aMCI	0.68	0.69	0.74	< 0.05	< 0.05	< 0.01e− 5
sd- vs md-aMCI	0.57	0.57	0.67	< 0.05	< 0.05	< 0.01e− 5