Brain Machine Interfaces (BMIs) rely on the correlation between neural activity and movement kinematics. However, many characteristics of the neural activity change after switching from pole control to brain control. Of particular interest are changes in the preferred direction (PD), and whether they reflect adaptation to the BMI filter. Here we investigate changes in the PD of simulated neurons that encode signals that are relevant for state estimation and control with the framework of optimal feedback control (OFC). Simulated BMI experiments based on the OFC framework demonstrate that changes in the PD may occur even with no adaptation. Further theoretical and simulations indicate the conditions under which there is no change in the PD upon switching to brain control Insights gained from this research can be used to improve the design of BMI filter - not only to minimize reconstruction error during pole control, but also to endow the neurons with desired PDs in brain control.