Bivariate linear models in neurobiology: problems of concept and methodology

Bivariate linear models, used to describe morphological and functional characteristics between two sets of observations, are examined both in concept and in application. This paper focuses on the underlying assumptions and statistics of the method most frequently used: ordinary linear regression, principal axis and standard major axis. It is shown how the choice of method should depend on: the purpose of the analysis and the a priori assumptions regarding the residual variance. It appears that none of the methods has a universal application. Differences among the models discussed are illustrated by a bivariate morphometric analysis of cerebrocortical regions in primates.