Table 1.

Chronological age prediction accuracy for the considered methods.

	Type	Method	Val MAE	Val $R^2$	Test MAE	Test $R^2$	No. I
(A)	T1-weighted	CNN	3.996	0.810	4.006	0.829	1815
	Jacobian	CNN	4.801	0.710	4.804	0.758	1815
	Gray matter	CNN	4.766	0.721	4.641	0.776	1815
	White matter	CNN	4.676	0.735	4.189	0.812	1815
(B)	MV (T1 and JM)	CNN	4.102	0.803	3.919	0.841	1815
	MV (GM and WM)	CNN	4.172	0.790	3.674	0.849	1815
	MV (T1, JM, and GM)	CNN	3.964	0.813	3.838	0.847	1815
	MV (T1, JM, GM, and WM)	CNN	3.845	0.849	3.584	0.849	1815
	LRB (T1, JM, GM, and WM)	CNN	3.581	0.847	3.388	0.872	1815
(C)	SBM	RR	5.268	0.689	5.176	0.697	1320
	VBM	GPR	4.278	0.781	4.317	0.766	1794
	SM	RR	4.898	0.722	4.937	0.728	1815
	MV (SBM, VBM, and SM)	GPR/RR	4.008	0.808	3.940	0.761	1246
	LRB (SBM, VBM, and SM)	GPR/RR	3.906	0.812	3.849	0.766	1246

(A) The best results are shown in bold. (B) The training/validation/test split is the same as for (A).

(C) The cross validation was performed using 10-fold cross validation. The SBM feature training/test split was 1056/264, the VBM feature training/test split was 1438/356, and the SM feature training/test split was 1469/346

(A) The performance of the CNNs that were trained using T1-weighted images, Jacobian maps, GM and WM segmented images. Training set ($N=1171$), validation set ($N=298$), and test set ($N=346$). (B) The performance when combining CNN predictions. (C) The results of the best methods trained on SBM, VBM and similarity matrix features

CV cross validation, GM gray matter, I images, JM Jacobian map, LRB linear regression blender, MV majority voting, MAE mean absolute error, RR ridge regression, SM similarity matrix, SBM surface-based morphometry, val validation, VBM voxel-based morphometry, WM white matter