. Author manuscript; available in PMC: 2014 Sep 1.

Published in final edited form as: Dev Sci. 2013 Jun 25;16(5):653–664. doi: 10.1111/desc.12077

Table 5.

Hierarchical Regressions of Age and Fractional Anisotropy Predicting Cognitive Control

Dependent Variable:	Step	Model	R² change	Sig F change	Beta	p	Tolerance

Cognitive control	1	Age	0.000	0.948	-.010	0.948	1

	2	Age			.052	0.718	0.973
		FA - SLF	0.136	0.012	-.374	0.012	0.973

	1	Age	0.000	0.948	-.010	0.948	1

	2	Age			.047	0.746	0.971
		FA – CB	0.107	0.026	-.333	0.026	0.971

	1	Age	0.000	0.948	-.010	0.948	1

	2	Age			.023	0.879	0.981
		FA – ACR	0.054	0.120	-.235	0.12	0.981

	1	Age	0.000	0.948	-.010	0.948	1

	2	Age			.031	0.836	0.984
		FA - uncinate	0.028	0.264	-.170	0.264	0.984

Three hierarchical regressions were conducted, with age and then fractional anisotropy added as the independent variables. In each case, the dependent variable was performance on a Stroop-like task. When adjusting for age, fractional anisotropy in the superior longitudinal fasiculus significantly predicted cognitive control, and fractional anisotropy in the cingulum bundle showed a borderline effect when considering multiple comparisons (alpha set at 0.0125). In contrast, FA in the anterior corona radiata and uncinate fasiculus were not related to cognitive control. Age and FA do not exhibit multicollinearity. FA=fractional anisotropy. SLF=superior longitudinal fasciculus. CB= cingulum bundle. ACR=anterior corona radiata.