Table 4.

Model-fits of brain volumes, cortical thickness and surface area measures with age.

Brain structure	Model fit
	Linear			Quadratica
	R-square			R-square
	All	Males	Females	All	Males	Females
Amygdala	0.0003
Caudate nucleus	0.001
Nucleus accumbens	0.007
Hippocampus	0.001
Pallidum	0.001
Putamen	0.06***
Thalamus	0.028***
Lateral ventricles	0.123***
3rd ventricle	0.053***	0.066***	0.046**
4th ventricle	0.005
Cerebellum GM	0.088***
Cerebellum WM	0.073***
Total GMb	0.283***	0.43***	0.15***	0.324***
Total WMb	0.193***	0.13***	0.278***
Intracranium	0.01*	0.05	0.026**
Total brain	0.029***
ACC surface area	0.0002	0.015	0.011
ACC GMb	0.114***			0.123***
acc thickness	0.215***
Frontal surface areab	0.04***	0.144***	0.0001	0.055***	0.161***
Frontal GMb	0.383***	0.49***	0.274***	0.405***	0.518***
Frontal thickness	0.288***
Temporal surface areab	0.027**	0.103***	0.0001	0.06***	0.125***
Temporal GMb	0.166***	0.255***	0.089***	0.198***	0.285***	0.114**
Temporal thickness	0.169***
Parietal surface areab	0.08***	0.211***	0.007	0.142***	0.273***
Parietal GMb	0.401***	0.506***	0.296***	0.471***	0.574***	0.354***
Parietal thickness	0.404***			0.416***
Occipital surface areab	0.011*			0.055***
Occipital GMb	0.192***	0.261***	0.123***	0.256***	0.318***	0.185***
Occipital thickness	0.389***			0.411***

Abbreviations: GM, gray matter; WM, white matter. A non-significant R-square indicates that the regression line is not different from zero.

^aAll quadratic fits were significantly better than linear fits based on F-ratio derived from “extra sum-of-squares method”. Obtained p-values of the F-ratio indicate if the simpler linear model is really correct, and the chance that randomly obtained data would show a better fit to a more complicated (quadratic) model. Low p-values indicate that the quadratic model is significantly better than the linear model Motulsky and Christopoulos (2004, p. 141).

^bTrajectories differed significantly between sexes.

^*p < 0.05.

^**p < 0.01.

^***p < 0.001.