Symbolic regression (SR) is one the most popular applications of genetic programming (GP) and an attractive alternative to the standard deterministic regression approaches due to its flexibility in generating free-form mathematical models from observed data without any domain knowledge. However, GP suffers from various issues hindering the applicability of the technique to real-life problems. In this paper, we show that a hybrid deterministic regression (DR)/genetic programming based symbolic regression (GP-SR) algorithm outperforms GP-SR alone on a brain imaging dataset.