Hi Paul,

> how the shared variance in age and HVA could relate to the imaging indices?

How to quantify the relatedness in this context? One approach is to use the coefficient of determination R^2 in a regression model that measures the proportion of the variation in the response variable Z that is predictable from the explanatory variable(s).

With two explanatory variables X and Y, you can construct three separate models for the response variable Z:

1) Z ~ X,
2) Z ~ Y, and
3) Z ~ X + Y.

Then obtain R_0x^2 from model 1), R_0y^2 from model 2), and R_1x^2 plus R_2y^2 from model 3). Presumably,

R_0x^2 + R_0y^2 ≥ R_1x^2 + R_2y^2

Use 3dRegAna to obtain these R^2 values, and this is why I previously suggested 3dRegAna. Check out the 3dRegAna help (and maybe the manual [afni.nimh.nih.gov]) for details.

So, the following differences

(R_0x^2 + R_0y^2) - (R_1x^2 + R_2y^2),
R_0x^2 - R_1x^2
R_0y^2 - R_2y^2

would help you partition and assess each variable's unique and shared contribution in predicting Z?

Gang