Table 2.

Ridge Regression Performance Statistics in the (A) Training Data Set and (B) Test Data Set

				$\widehat{\mathit{B}\mathit{A}}$
				Uncorrected model				Corrected model
Measure	$m$ *	Sex	$n$ †	$r$ ‡	${\overline{R}}^2$ §	$MSE$ ‖ (y2)	$MAE$ ¶ (y)	$r$ ‡	${\overline{R}}^2$ §	$MSE$ ‖ (y2)	$MAE$ ¶ (y)
(A) Training data
Volume	83	m&f	3 418	0.90	0.82	69.36	6.66	0.92	0.81	70.94	6.57
		m	1 565	0.93	0.87	57.52	6.16	0.94	0.87	57.03	6.01
		f	1 853	0.69	0.05	340.33	16.10	1.00	1.00	0.01	0.08
Surface area	74	m&f	3 418	0.82	0.67	123.61	8.89	0.86	0.67	125.30	8.88
		m	1 565	0.54	0.05	405.97	17.77	1.00	1.00	0.01	0.08
		f	1 853	0.45	0.04	341.59	16.11	1.00	1.00	0.01	0.08
Mean thickness	74	m&f	3 418	0.88	0.78	85.25	7.27	0.90	0.77	87.40	7.28
		m	1 565	0.70	0.05	405.39	17.76	1.00	1.00	0.01	0.06
		f	1 853	0.59	0.04	341.52	16.12	1.00	1.00	0.01	0.06
Mean curvature	74	m&f	3 418	0.88	0.77	86.39	7.43	0.90	0.77	86.34	7.37
		m	1 565	0.89	0.80	84.39	7.33	0.91	0.81	82.21	7.17
		f	1 853	0.87	0.77	83.38	7.24	0.90	0.77	82.69	7.19
(B) Testing data
Volume	83	m&f	651	0.86	0.74	100.60	8.25	0.90	0.79	81.96	7.00
		m	332	0.87	0.68	147.30	9.62	0.93	0.88	54.16	5.63
		f	328	0.78	0.25	354.41	16.17	1.00	1.00	0.00	0.04
Surface area	74	m&f	651	0.54	0.36	247.95	12.87	0.70	0.08	356.13	14.85
		m	332	0.57	0.22	342.20	16.00	1.00	1.00	0.01	0.07
		f	328	0.50	0.22	354.98	16.19	1.00	1.00	0.01	0.06
Mean thickness	74	m&f	651	0.70	0.55	175.69	10.89	0.91	0.82	71.82	6.89
		m	332	0.39	0.22	342.67	16.01	1.00	1.00	0.02	0.08
		f	328	0.42	0.22	355.73	16.20	1.00	1.00	0.01	0.07
Mean curvature	74	m&f	651	0.76	0.57	166.42	10.62	0.86	0.68	123.19	8.74
		m	332	0.76	0.29	309.64	14.44	0.90	0.81	80.96	6.92
		f	328	0.74	0.36	291.80	13.71	0.91	0.84	71.82	6.89

Notes: $BA$ = biological age; f = female; m = male. Twelve models were fit, one for each combination of brain measure types (volume, surface area, mean thickness, and mean curvature) and sex-dependent model type (ie, males-only model, females-only model, and the sex-agnostic model with sex effects regressed out). Fitted values of $\widehat{\mathit{\beta }}$ were used to estimate $BA$ in the Cam-CAN independent test set.

*$m$ = number of model features

^† $n$ = sample size

^‡ $r$ = Pearson product-moment correlation coefficient between $\mathit{C}\mathit{A}$ and $\widehat{\mathit{B}\mathit{A}}$

^§ ${\overline{R}}^2$ = coefficient of determination

^‖ $MSE$ = mean squared error

^¶ $MAE$ = mean absolute error