Table 2.

Summary of the conducted experiments and the obtained results. Vec. dim. - dimensionality of the input feature vectors and indicative of whether dimensionality reduction was used or not; Strategy - indication of whether the functional and structural feature vectors were concatenated prior to classification or classified separately; Ensemble - indication of whether an ensemble of classifiers was or was not used, and if yes, how many classifiers were considered; * - label fusion was performed using the average of softmax probabilities; ** - label fusion was performed by majority voting, using average softmax probabilities only in the case of a tie). Values of mean accuracy and its standard deviation are shown in percentages. Sensitivity and specificity values are also shown. The best result is highlighted in bold.

Exp. no.	Pipeline	Atlas	Cases	Vec. dim.	Acc. mean	Acc. std	Sens.	Spec.
Functional data classification
1	functional	AAL	884	6670	64.46	4.73	0.53	0.72
2	functional	AAL	884	3000	67.96	3.50	0.60	0.73
3	functional	CC200	884	3000	70.37	4.88	0.64	0.75
4	ensemble of 5	CC200	884	3000	74.90	3.48	0.74	0.76
Structural data classification
5	structural	Destrieux	1014	10,878	52.27	3.51	0.47	0.55
6	structural	Destrieux	1014	3000	77.41	3.99	0.71	0.76
7	ensemble of 5	Destrieux	1014	3000	78.69	2.86	0.78	0.79
Combined data classification
Exp. no.	Strategy	Ensemble	Cases	Vec. dim.	Acc. mean	Acc. std	Sens.	Spec.
8	concatenate	no	817	6000	70.38	2.17	0.62	0.76
9	concatenate	no	817	3000	70.37	4.01	0.65	0.77
10	concatenate	yes (5)	817	6000	73.44	4.84	0.69	0.77
11	concatenate	yes (5)	817	3000	73.31	3.86	0.71	0.75
12	separate	no	817	3000	82.97	3.72	0.82	0.84
13	separate*	yes (5+5)	817	3000	85.06	3.52	0.81	0.89
14	separate**	yes (5+5)	817	3000	84.45	2.90	0.80	0.88