The approach we advocate here is an extension of the single-trial correlation approach. Multiple regression has several important advantages over correlation. First, many independent variables can be entered into the regression, and variance due to different variables can be parceled out. Second, interaction terms between variables can be constructed to estimate possible non-linear effects of combinations of experimental and behavior variables. Third, with multiple regression one can examine not only the slopes (i.e., linear relationship between two variables) but also the intercept of the model (“DC” or mean-offset of the relationship), which may be useful for dissociating random (i.e., idiosyncratic relationships with an independent variable) versus fixed (i.e., all subjects show a general increase in brain activity as a function of condition) effects. Forth, related extensions to multiple regression such as hierarchical linear modeling are amenable for more in-depth analyses into differences across groups of subjects.
